Overview

Dataset statistics

Number of variables14
Number of observations45556
Missing cells177031
Missing cells (%)27.8%
Duplicate rows158
Duplicate rows (%)0.3%
Total size in memory5.2 MiB
Average record size in memory120.0 B

Variable types

Unsupported1
Text12
Categorical1

Alerts

Dataset has 158 (0.3%) duplicate rowsDuplicates
source_id has 38711 (85.0%) missing valuesMissing
author has 7004 (15.4%) missing valuesMissing
url_to_image has 2826 (6.2%) missing valuesMissing
full_content has 42986 (94.4%) missing valuesMissing
article has 42584 (93.5%) missing valuesMissing
title_sentiment has 42584 (93.5%) missing valuesMissing
article_id is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-15 15:56:38.905856
Analysis finished2024-02-15 15:57:40.685357
Duration1 minute and 1.78 second
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

article_id
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size711.8 KiB

source_id
Text

MISSING 

Distinct86
Distinct (%)1.3%
Missing38711
Missing (%)85.0%
Memory size711.8 KiB
2024-02-15T15:57:41.042189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length25
Mean length11.048064
Min length2

Characters and Unicode

Total characters75624
Distinct characters45
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st rowal-jazeera-english
2nd rowabc-news-au
3rd rowbbc-news
4th rowthe-times-of-india
5th rowthe-jerusalem-post
ValueCountFrequency (%)
the-times-of-india 1126
16.4%
bbc-news 720
 
10.5%
al-jazeera-english 555
 
8.1%
rt 350
 
5.1%
abc-news 339
 
5.0%
business-insider 330
 
4.8%
newsweek 275
 
4.0%
abc-news-au 193
 
2.8%
time 179
 
2.6%
espn 178
 
2.6%
Other values (77) 2602
38.0%
2024-02-15T15:57:41.869187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10086
13.3%
- 8419
11.1%
s 6751
 
8.9%
i 6490
 
8.6%
n 5932
 
7.8%
a 4920
 
6.5%
t 4641
 
6.1%
b 3126
 
4.1%
r 2908
 
3.8%
c 2646
 
3.5%
Other values (35) 19705
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66974
88.6%
Dash Punctuation 8419
 
11.1%
Decimal Number 206
 
0.3%
Uppercase Letter 14
 
< 0.1%
Other Punctuation 9
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10086
15.1%
s 6751
 
10.1%
i 6490
 
9.7%
n 5932
 
8.9%
a 4920
 
7.3%
t 4641
 
6.9%
b 3126
 
4.7%
r 2908
 
4.3%
c 2646
 
4.0%
w 2582
 
3.9%
Other values (15) 16892
25.2%
Uppercase Letter
ValueCountFrequency (%)
S 3
21.4%
A 3
21.4%
U 2
14.3%
C 1
 
7.1%
T 1
 
7.1%
D 1
 
7.1%
H 1
 
7.1%
P 1
 
7.1%
G 1
 
7.1%
Decimal Number
ValueCountFrequency (%)
4 97
47.1%
2 96
46.6%
3 8
 
3.9%
7 2
 
1.0%
1 2
 
1.0%
0 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/ 5
55.6%
. 3
33.3%
: 1
 
11.1%
Dash Punctuation
ValueCountFrequency (%)
- 8419
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66988
88.6%
Common 8636
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10086
15.1%
s 6751
 
10.1%
i 6490
 
9.7%
n 5932
 
8.9%
a 4920
 
7.3%
t 4641
 
6.9%
b 3126
 
4.7%
r 2908
 
4.3%
c 2646
 
3.9%
w 2582
 
3.9%
Other values (24) 16906
25.2%
Common
ValueCountFrequency (%)
- 8419
97.5%
4 97
 
1.1%
2 96
 
1.1%
3 8
 
0.1%
/ 5
 
0.1%
. 3
 
< 0.1%
7 2
 
< 0.1%
1 2
 
< 0.1%
2
 
< 0.1%
: 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10086
13.3%
- 8419
11.1%
s 6751
 
8.9%
i 6490
 
8.6%
n 5932
 
7.8%
a 4920
 
6.5%
t 4641
 
6.1%
b 3126
 
4.1%
r 2908
 
3.8%
c 2646
 
3.5%
Other values (35) 19705
26.1%
Distinct2160
Distinct (%)4.7%
Missing13
Missing (%)< 0.1%
Memory size711.8 KiB
2024-02-15T15:57:42.420450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length63
Median length32
Mean length12.473025
Min length2

Characters and Unicode

Total characters568059
Distinct characters84
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique597 ?
Unique (%)1.3%

Sample

1st rowInternational Business Times
2nd rowPrtimes.jp
3rd rowVOA News
4th rowThe Indian Express
5th rowThe Times of Israel
ValueCountFrequency (%)
news 4618
 
6.5%
the 3538
 
5.0%
biztoc.com 2065
 
2.9%
daily 2057
 
2.9%
marketscreener.com 2046
 
2.9%
globenewswire 1786
 
2.5%
times 1772
 
2.5%
etf 1596
 
2.3%
forbes 1361
 
1.9%
of 1253
 
1.8%
Other values (2341) 48531
68.7%
2024-02-15T15:57:43.300381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 60453
 
10.6%
o 42297
 
7.4%
r 31353
 
5.5%
s 31217
 
5.5%
i 31141
 
5.5%
a 26814
 
4.7%
n 25620
 
4.5%
c 25251
 
4.4%
25137
 
4.4%
t 22856
 
4.0%
Other values (74) 245920
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 426948
75.2%
Uppercase Letter 91118
 
16.0%
Space Separator 25137
 
4.4%
Other Punctuation 20311
 
3.6%
Open Punctuation 1266
 
0.2%
Close Punctuation 1266
 
0.2%
Decimal Number 1202
 
0.2%
Dash Punctuation 768
 
0.1%
Math Symbol 39
 
< 0.1%
Final Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60453
14.2%
o 42297
 
9.9%
r 31353
 
7.3%
s 31217
 
7.3%
i 31141
 
7.3%
a 26814
 
6.3%
n 25620
 
6.0%
c 25251
 
5.9%
t 22856
 
5.4%
m 19419
 
4.5%
Other values (20) 110527
25.9%
Uppercase Letter
ValueCountFrequency (%)
T 11211
12.3%
N 10505
 
11.5%
B 8101
 
8.9%
C 5965
 
6.5%
A 5561
 
6.1%
E 4902
 
5.4%
F 4807
 
5.3%
S 4718
 
5.2%
D 4561
 
5.0%
I 4455
 
4.9%
Other values (16) 26332
28.9%
Decimal Number
ValueCountFrequency (%)
2 306
25.5%
4 269
22.4%
5 130
10.8%
9 120
 
10.0%
0 106
 
8.8%
1 86
 
7.2%
3 74
 
6.2%
7 62
 
5.2%
6 47
 
3.9%
8 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 20046
98.7%
' 158
 
0.8%
/ 62
 
0.3%
* 16
 
0.1%
! 15
 
0.1%
: 10
 
< 0.1%
& 3
 
< 0.1%
@ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 834
65.9%
( 432
34.1%
Close Punctuation
ValueCountFrequency (%)
] 834
65.9%
) 432
34.1%
Math Symbol
ValueCountFrequency (%)
+ 36
92.3%
| 3
 
7.7%
Space Separator
ValueCountFrequency (%)
25137
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 768
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 518066
91.2%
Common 49993
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60453
 
11.7%
o 42297
 
8.2%
r 31353
 
6.1%
s 31217
 
6.0%
i 31141
 
6.0%
a 26814
 
5.2%
n 25620
 
4.9%
c 25251
 
4.9%
t 22856
 
4.4%
m 19419
 
3.7%
Other values (46) 201645
38.9%
Common
ValueCountFrequency (%)
25137
50.3%
. 20046
40.1%
[ 834
 
1.7%
] 834
 
1.7%
- 768
 
1.5%
( 432
 
0.9%
) 432
 
0.9%
2 306
 
0.6%
4 269
 
0.5%
' 158
 
0.3%
Other values (18) 777
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 567888
> 99.9%
None 168
 
< 0.1%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 60453
 
10.6%
o 42297
 
7.4%
r 31353
 
5.5%
s 31217
 
5.5%
i 31141
 
5.5%
a 26814
 
4.7%
n 25620
 
4.5%
c 25251
 
4.4%
25137
 
4.4%
t 22856
 
4.0%
Other values (69) 245749
43.3%
None
ValueCountFrequency (%)
é 112
66.7%
ü 48
28.6%
è 7
 
4.2%
ó 1
 
0.6%
Punctuation
ValueCountFrequency (%)
3
100.0%

author
Text

MISSING 

Distinct11270
Distinct (%)29.2%
Missing7004
Missing (%)15.4%
Memory size711.8 KiB
2024-02-15T15:57:43.853278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length746
Median length304
Mean length19.275031
Min length1

Characters and Unicode

Total characters743091
Distinct characters749
Distinct categories19 ?
Distinct scripts13 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5892 ?
Unique (%)15.3%

Sample

1st rowPaavan MATHEMA
2nd rowwebdesk@voanews.com (Agence France-Presse)
3rd rowEditorial
4th rowJacob Magid
5th rowhttps://www.facebook.com/bbcworldservice/
ValueCountFrequency (%)
news 2241
 
2.4%
contributor 1940
 
2.1%
marketbeat 1595
 
1.7%
john 923
 
1.0%
forbes 835
 
0.9%
staff 769
 
0.8%
pike 658
 
0.7%
the 600
 
0.6%
and 600
 
0.6%
press 521
 
0.6%
Other values (15217) 83713
88.7%
2024-02-15T15:57:44.809398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 63818
 
8.6%
57215
 
7.7%
a 53367
 
7.2%
o 43121
 
5.8%
r 42976
 
5.8%
n 40581
 
5.5%
i 38702
 
5.2%
t 36317
 
4.9%
s 35672
 
4.8%
l 23163
 
3.1%
Other values (739) 308159
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 535111
72.0%
Uppercase Letter 111248
 
15.0%
Space Separator 57265
 
7.7%
Other Punctuation 30257
 
4.1%
Control 2782
 
0.4%
Other Letter 1466
 
0.2%
Decimal Number 1317
 
0.2%
Open Punctuation 1078
 
0.1%
Close Punctuation 1076
 
0.1%
Dash Punctuation 1063
 
0.1%
Other values (9) 428
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36
 
2.5%
30
 
2.0%
ا 27
 
1.8%
27
 
1.8%
ی 23
 
1.6%
22
 
1.5%
22
 
1.5%
22
 
1.5%
ر 21
 
1.4%
20
 
1.4%
Other values (435) 1216
82.9%
Lowercase Letter
ValueCountFrequency (%)
e 63818
11.9%
a 53367
 
10.0%
o 43121
 
8.1%
r 42976
 
8.0%
n 40581
 
7.6%
i 38702
 
7.2%
t 36317
 
6.8%
s 35672
 
6.7%
l 23163
 
4.3%
c 18978
 
3.5%
Other values (132) 138416
25.9%
Uppercase Letter
ValueCountFrequency (%)
S 9714
 
8.7%
M 8955
 
8.0%
A 8595
 
7.7%
C 7714
 
6.9%
B 6641
 
6.0%
R 6155
 
5.5%
N 6036
 
5.4%
P 5840
 
5.2%
T 5752
 
5.2%
J 5171
 
4.6%
Other values (80) 40675
36.6%
Other Punctuation
ValueCountFrequency (%)
. 10255
33.9%
/ 8993
29.7%
, 7762
25.7%
: 1902
 
6.3%
@ 915
 
3.0%
' 219
 
0.7%
& 116
 
0.4%
! 36
 
0.1%
" 32
 
0.1%
8
 
< 0.1%
Other values (7) 19
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 308
23.4%
1 304
23.1%
0 173
13.1%
4 166
12.6%
3 83
 
6.3%
9 83
 
6.3%
7 74
 
5.6%
5 53
 
4.0%
8 42
 
3.2%
6 31
 
2.4%
Spacing Mark
ValueCountFrequency (%)
21
47.7%
ि 11
25.0%
5
 
11.4%
3
 
6.8%
1
 
2.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
Nonspacing Mark
ValueCountFrequency (%)
17
34.0%
̈ 9
18.0%
8
16.0%
7
14.0%
4
 
8.0%
3
 
6.0%
1
 
2.0%
1
 
2.0%
Math Symbol
ValueCountFrequency (%)
| 29
38.7%
> 16
21.3%
< 16
21.3%
= 8
 
10.7%
4
 
5.3%
+ 2
 
2.7%
Other Symbol
ValueCountFrequency (%)
® 35
46.1%
14
 
18.4%
© 14
 
18.4%
13
 
17.1%
Space Separator
ValueCountFrequency (%)
57215
99.9%
25
 
< 0.1%
  25
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1048
97.2%
[ 16
 
1.5%
14
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 1046
97.2%
] 16
 
1.5%
14
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 1054
99.2%
9
 
0.8%
Final Punctuation
ValueCountFrequency (%)
48
96.0%
2
 
4.0%
Format
ValueCountFrequency (%)
8
72.7%
3
 
27.3%
Control
ValueCountFrequency (%)
2782
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 67
100.0%
Modifier Letter
ValueCountFrequency (%)
53
100.0%
Initial Punctuation
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 643716
86.6%
Common 95169
 
12.8%
Cyrillic 1705
 
0.2%
Greek 938
 
0.1%
Han 584
 
0.1%
Katakana 340
 
< 0.1%
Devanagari 210
 
< 0.1%
Arabic 192
 
< 0.1%
Hiragana 109
 
< 0.1%
Hangul 76
 
< 0.1%
Other values (3) 52
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
22
 
3.8%
22
 
3.8%
20
 
3.4%
19
 
3.3%
17
 
2.9%
12
 
2.1%
10
 
1.7%
10
 
1.7%
8
 
1.4%
8
 
1.4%
Other values (246) 436
74.7%
Latin
ValueCountFrequency (%)
e 63818
 
9.9%
a 53367
 
8.3%
o 43121
 
6.7%
r 42976
 
6.7%
n 40581
 
6.3%
i 38702
 
6.0%
t 36317
 
5.6%
s 35672
 
5.5%
l 23163
 
3.6%
c 18978
 
2.9%
Other values (117) 247021
38.4%
Cyrillic
ValueCountFrequency (%)
а 242
 
14.2%
р 116
 
6.8%
н 113
 
6.6%
и 113
 
6.6%
о 102
 
6.0%
е 92
 
5.4%
к 85
 
5.0%
в 72
 
4.2%
л 68
 
4.0%
т 58
 
3.4%
Other values (49) 644
37.8%
Katakana
ValueCountFrequency (%)
36
 
10.6%
30
 
8.8%
27
 
7.9%
14
 
4.1%
13
 
3.8%
13
 
3.8%
13
 
3.8%
11
 
3.2%
11
 
3.2%
9
 
2.6%
Other values (48) 163
47.9%
Common
ValueCountFrequency (%)
57215
60.1%
. 10255
 
10.8%
/ 8993
 
9.4%
, 7762
 
8.2%
2782
 
2.9%
: 1902
 
2.0%
- 1054
 
1.1%
( 1048
 
1.1%
) 1046
 
1.1%
@ 915
 
1.0%
Other values (45) 2197
 
2.3%
Greek
ValueCountFrequency (%)
α 118
 
12.6%
ο 72
 
7.7%
ς 70
 
7.5%
τ 63
 
6.7%
ν 63
 
6.7%
ρ 49
 
5.2%
ι 48
 
5.1%
λ 31
 
3.3%
η 31
 
3.3%
Σ 27
 
2.9%
Other values (36) 366
39.0%
Devanagari
ValueCountFrequency (%)
22
 
10.5%
21
 
10.0%
17
 
8.1%
14
 
6.7%
11
 
5.2%
ि 11
 
5.2%
10
 
4.8%
9
 
4.3%
8
 
3.8%
7
 
3.3%
Other values (27) 80
38.1%
Hiragana
ValueCountFrequency (%)
15
13.8%
9
 
8.3%
9
 
8.3%
8
 
7.3%
8
 
7.3%
7
 
6.4%
5
 
4.6%
5
 
4.6%
4
 
3.7%
3
 
2.8%
Other values (22) 36
33.0%
Arabic
ValueCountFrequency (%)
ا 27
14.1%
ی 23
 
12.0%
ر 21
 
10.9%
ن 12
 
6.2%
ب 11
 
5.7%
ل 11
 
5.7%
ح 9
 
4.7%
م 7
 
3.6%
ص 7
 
3.6%
و 6
 
3.1%
Other values (17) 58
30.2%
Hangul
ValueCountFrequency (%)
14
18.4%
10
13.2%
10
13.2%
8
10.5%
4
 
5.3%
4
 
5.3%
4
 
5.3%
4
 
5.3%
2
 
2.6%
2
 
2.6%
Other values (13) 14
18.4%
Malayalam
ValueCountFrequency (%)
3
13.0%
3
13.0%
2
 
8.7%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (6) 6
26.1%
Hebrew
ValueCountFrequency (%)
י 4
23.5%
א 2
11.8%
ר 2
11.8%
מ 2
11.8%
ב 1
 
5.9%
ת 1
 
5.9%
כ 1
 
5.9%
נ 1
 
5.9%
ו 1
 
5.9%
ל 1
 
5.9%
Inherited
ValueCountFrequency (%)
̈ 9
75.0%
3
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 736653
99.1%
None 2981
 
0.4%
Cyrillic 1705
 
0.2%
CJK 584
 
0.1%
Katakana 395
 
0.1%
Devanagari 210
 
< 0.1%
Arabic 192
 
< 0.1%
Hiragana 109
 
< 0.1%
Punctuation 97
 
< 0.1%
Hangul 76
 
< 0.1%
Other values (7) 89
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 63818
 
8.7%
57215
 
7.8%
a 53367
 
7.2%
o 43121
 
5.9%
r 42976
 
5.8%
n 40581
 
5.5%
i 38702
 
5.3%
t 36317
 
4.9%
s 35672
 
4.8%
l 23163
 
3.1%
Other values (78) 301721
41.0%
None
ValueCountFrequency (%)
é 309
 
10.4%
á 221
 
7.4%
í 189
 
6.3%
ó 164
 
5.5%
ñ 162
 
5.4%
ü 139
 
4.7%
α 118
 
4.0%
ö 97
 
3.3%
ο 72
 
2.4%
ς 70
 
2.3%
Other values (116) 1440
48.3%
Cyrillic
ValueCountFrequency (%)
а 242
 
14.2%
р 116
 
6.8%
н 113
 
6.6%
и 113
 
6.6%
о 102
 
6.0%
е 92
 
5.4%
к 85
 
5.0%
в 72
 
4.2%
л 68
 
4.0%
т 58
 
3.4%
Other values (49) 644
37.8%
Katakana
ValueCountFrequency (%)
53
 
13.4%
36
 
9.1%
30
 
7.6%
27
 
6.8%
14
 
3.5%
13
 
3.3%
13
 
3.3%
13
 
3.3%
11
 
2.8%
11
 
2.8%
Other values (50) 174
44.1%
Punctuation
ValueCountFrequency (%)
48
49.5%
25
25.8%
9
 
9.3%
8
 
8.2%
3
 
3.1%
2
 
2.1%
2
 
2.1%
Arabic
ValueCountFrequency (%)
ا 27
14.1%
ی 23
 
12.0%
ر 21
 
10.9%
ن 12
 
6.2%
ب 11
 
5.7%
ل 11
 
5.7%
ح 9
 
4.7%
م 7
 
3.6%
ص 7
 
3.6%
و 6
 
3.1%
Other values (17) 58
30.2%
CJK
ValueCountFrequency (%)
22
 
3.8%
22
 
3.8%
20
 
3.4%
19
 
3.3%
17
 
2.9%
12
 
2.1%
10
 
1.7%
10
 
1.7%
8
 
1.4%
8
 
1.4%
Other values (246) 436
74.7%
Devanagari
ValueCountFrequency (%)
22
 
10.5%
21
 
10.0%
17
 
8.1%
14
 
6.7%
11
 
5.2%
ि 11
 
5.2%
10
 
4.8%
9
 
4.3%
8
 
3.8%
7
 
3.3%
Other values (27) 80
38.1%
Hiragana
ValueCountFrequency (%)
15
13.8%
9
 
8.3%
9
 
8.3%
8
 
7.3%
8
 
7.3%
7
 
6.4%
5
 
4.6%
5
 
4.6%
4
 
3.7%
3
 
2.8%
Other values (22) 36
33.0%
Hangul
ValueCountFrequency (%)
14
18.4%
10
13.2%
10
13.2%
8
10.5%
4
 
5.3%
4
 
5.3%
4
 
5.3%
4
 
5.3%
2
 
2.6%
2
 
2.6%
Other values (13) 14
18.4%
Specials
ValueCountFrequency (%)
14
100.0%
Letterlike Symbols
ValueCountFrequency (%)
13
100.0%
Phonetic Ext
ValueCountFrequency (%)
10
100.0%
Diacriticals
ValueCountFrequency (%)
̈ 9
100.0%
Hebrew
ValueCountFrequency (%)
י 4
23.5%
א 2
11.8%
ר 2
11.8%
מ 2
11.8%
ב 1
 
5.9%
ת 1
 
5.9%
כ 1
 
5.9%
נ 1
 
5.9%
ו 1
 
5.9%
ל 1
 
5.9%
Malayalam
ValueCountFrequency (%)
3
13.0%
3
13.0%
2
 
8.7%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (6) 6
26.1%
Latin Ext Additional
ValueCountFrequency (%)
1
33.3%
ế 1
33.3%
1
33.3%

title
Text

Distinct30876
Distinct (%)67.8%
Missing33
Missing (%)0.1%
Memory size711.8 KiB
2024-02-15T15:57:45.418112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length590
Median length249
Mean length71.767678
Min length5

Characters and Unicode

Total characters3267080
Distinct characters2937
Distinct categories22 ?
Distinct scripts15 ?
Distinct blocks27 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24006 ?
Unique (%)52.7%

Sample

1st rowUN Chief Urges World To 'Stop The Madness' Of Climate Change
2nd rowRANDEBOOよりワンランク上の大人っぽさが漂うニットとベストが新登場。
3rd rowUN Chief Urges World to 'Stop the Madness' of Climate Change
4th rowSikkim warning: Hydroelectricity push must be accompanied by safety measures
5th row200 foreigners, dual nationals cut down in Hamas assault. Here’s where they were from
ValueCountFrequency (%)
to 11222
 
2.2%
the 9427
 
1.8%
in 8892
 
1.7%
of 7610
 
1.5%
and 6159
 
1.2%
for 5860
 
1.1%
a 5494
 
1.1%
5126
 
1.0%
on 3781
 
0.7%
with 3045
 
0.6%
Other values (60831) 446415
87.0%
2024-02-15T15:57:46.682292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
466715
 
14.3%
e 274258
 
8.4%
a 208839
 
6.4%
i 183804
 
5.6%
n 177349
 
5.4%
o 173652
 
5.3%
t 168184
 
5.1%
r 166093
 
5.1%
s 162424
 
5.0%
l 108147
 
3.3%
Other values (2927) 1177615
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2285370
70.0%
Space Separator 467578
 
14.3%
Uppercase Letter 318669
 
9.8%
Decimal Number 60351
 
1.8%
Other Punctuation 55027
 
1.7%
Other Letter 38097
 
1.2%
Dash Punctuation 11769
 
0.4%
Final Punctuation 8202
 
0.3%
Open Punctuation 5462
 
0.2%
Close Punctuation 5327
 
0.2%
Other values (12) 11228
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1034
 
2.7%
931
 
2.4%
620
 
1.6%
600
 
1.6%
585
 
1.5%
523
 
1.4%
502
 
1.3%
485
 
1.3%
480
 
1.3%
458
 
1.2%
Other values (2377) 31879
83.7%
Lowercase Letter
ValueCountFrequency (%)
e 274258
12.0%
a 208839
 
9.1%
i 183804
 
8.0%
n 177349
 
7.8%
o 173652
 
7.6%
t 168184
 
7.4%
r 166093
 
7.3%
s 162424
 
7.1%
l 108147
 
4.7%
d 77013
 
3.4%
Other values (199) 585607
25.6%
Uppercase Letter
ValueCountFrequency (%)
S 30054
 
9.4%
A 27085
 
8.5%
C 21787
 
6.8%
T 21718
 
6.8%
I 17772
 
5.6%
M 17079
 
5.4%
P 15792
 
5.0%
B 15674
 
4.9%
R 15438
 
4.8%
E 15141
 
4.8%
Other values (110) 121129
38.0%
Nonspacing Mark
ValueCountFrequency (%)
183
21.3%
152
17.7%
109
12.7%
52
 
6.1%
46
 
5.4%
37
 
4.3%
31
 
3.6%
31
 
3.6%
25
 
2.9%
22
 
2.6%
Other values (26) 171
19.9%
Other Punctuation
ValueCountFrequency (%)
, 14157
25.7%
: 12597
22.9%
. 8695
15.8%
' 8556
15.5%
? 2256
 
4.1%
" 1574
 
2.9%
% 1538
 
2.8%
& 1346
 
2.4%
/ 790
 
1.4%
! 706
 
1.3%
Other values (24) 2812
 
5.1%
Decimal Number
ValueCountFrequency (%)
2 14920
24.7%
0 13039
21.6%
1 7645
12.7%
3 7222
12.0%
5 4014
 
6.7%
4 3892
 
6.4%
9 2499
 
4.1%
8 2399
 
4.0%
6 2361
 
3.9%
7 2326
 
3.9%
Other values (15) 34
 
0.1%
Math Symbol
ValueCountFrequency (%)
| 927
57.0%
+ 495
30.4%
× 46
 
2.8%
40
 
2.5%
23
 
1.4%
23
 
1.4%
~ 13
 
0.8%
> 12
 
0.7%
< 12
 
0.7%
8
 
0.5%
Other values (10) 27
 
1.7%
Spacing Mark
ValueCountFrequency (%)
283
46.4%
130
21.3%
ि 102
 
16.7%
61
 
10.0%
7
 
1.1%
5
 
0.8%
5
 
0.8%
5
 
0.8%
ി 3
 
0.5%
3
 
0.5%
Other values (5) 6
 
1.0%
Other Symbol
ValueCountFrequency (%)
47
33.3%
® 45
31.9%
° 12
 
8.5%
8
 
5.7%
5
 
3.5%
4
 
2.8%
4
 
2.8%
4
 
2.8%
3
 
2.1%
3
 
2.1%
Other values (5) 6
 
4.3%
Open Punctuation
ValueCountFrequency (%)
( 3176
58.1%
[ 1151
 
21.1%
508
 
9.3%
178
 
3.3%
164
 
3.0%
138
 
2.5%
110
 
2.0%
{ 14
 
0.3%
13
 
0.2%
8
 
0.1%
Control
ValueCountFrequency (%)
38
41.8%
€ 27
29.7%
™ 8
 
8.8%
7
 
7.7%
“ 4
 
4.4%
— 2
 
2.2%
˜ 1
 
1.1%
‡ 1
 
1.1%
’ 1
 
1.1%
œ 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 3181
59.7%
] 1150
 
21.6%
506
 
9.5%
178
 
3.3%
164
 
3.1%
110
 
2.1%
} 14
 
0.3%
14
 
0.3%
8
 
0.2%
2
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
1527
99.1%
5
 
0.3%
ـ 3
 
0.2%
2
 
0.1%
1
 
0.1%
ʽ 1
 
0.1%
ʼ 1
 
0.1%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 10148
86.2%
1113
 
9.5%
489
 
4.2%
8
 
0.1%
7
 
0.1%
3
 
< 0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
466715
99.8%
  769
 
0.2%
  89
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 2596
92.3%
163
 
5.8%
£ 40
 
1.4%
12
 
0.4%
¢ 1
 
< 0.1%
Format
ValueCountFrequency (%)
20
32.8%
14
23.0%
­ 14
23.0%
11
18.0%
2
 
3.3%
Other Number
ValueCountFrequency (%)
½ 8
40.0%
¹ 7
35.0%
² 3
 
15.0%
2
 
10.0%
Final Punctuation
ValueCountFrequency (%)
7127
86.9%
841
 
10.3%
» 234
 
2.9%
Initial Punctuation
ValueCountFrequency (%)
2334
67.9%
869
 
25.3%
« 234
 
6.8%
Modifier Symbol
ValueCountFrequency (%)
4
50.0%
^ 3
37.5%
¸ 1
 
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2583239
79.1%
Common 623431
 
19.1%
Cyrillic 17139
 
0.5%
Han 14136
 
0.4%
Katakana 12571
 
0.4%
Hiragana 6632
 
0.2%
Greek 3683
 
0.1%
Devanagari 2861
 
0.1%
Arabic 1172
 
< 0.1%
Thai 1101
 
< 0.1%
Other values (5) 1115
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
292
 
2.1%
265
 
1.9%
171
 
1.2%
160
 
1.1%
153
 
1.1%
147
 
1.0%
142
 
1.0%
139
 
1.0%
126
 
0.9%
126
 
0.9%
Other values (1794) 12415
87.8%
Hangul
ValueCountFrequency (%)
38
 
4.6%
26
 
3.1%
24
 
2.9%
21
 
2.5%
18
 
2.2%
14
 
1.7%
14
 
1.7%
13
 
1.6%
12
 
1.4%
12
 
1.4%
Other values (221) 640
76.9%
Latin
ValueCountFrequency (%)
e 274258
 
10.6%
a 208839
 
8.1%
i 183804
 
7.1%
n 177349
 
6.9%
o 173652
 
6.7%
t 168184
 
6.5%
r 166093
 
6.4%
s 162424
 
6.3%
l 108147
 
4.2%
d 77013
 
3.0%
Other values (193) 883476
34.2%
Common
ValueCountFrequency (%)
466715
74.9%
2 14920
 
2.4%
, 14157
 
2.3%
0 13039
 
2.1%
: 12597
 
2.0%
- 10148
 
1.6%
. 8695
 
1.4%
' 8556
 
1.4%
1 7645
 
1.2%
3 7222
 
1.2%
Other values (150) 59737
 
9.6%
Katakana
ValueCountFrequency (%)
1034
 
8.2%
620
 
4.9%
600
 
4.8%
585
 
4.7%
485
 
3.9%
480
 
3.8%
426
 
3.4%
407
 
3.2%
386
 
3.1%
384
 
3.1%
Other values (72) 7164
57.0%
Hiragana
ValueCountFrequency (%)
931
 
14.0%
523
 
7.9%
502
 
7.6%
458
 
6.9%
316
 
4.8%
260
 
3.9%
225
 
3.4%
210
 
3.2%
208
 
3.1%
206
 
3.1%
Other values (63) 2793
42.1%
Devanagari
ValueCountFrequency (%)
283
 
9.9%
189
 
6.6%
183
 
6.4%
175
 
6.1%
152
 
5.3%
130
 
4.5%
118
 
4.1%
109
 
3.8%
109
 
3.8%
104
 
3.6%
Other values (55) 1309
45.8%
Cyrillic
ValueCountFrequency (%)
о 1706
 
10.0%
и 1405
 
8.2%
а 1397
 
8.2%
е 1214
 
7.1%
н 1056
 
6.2%
с 1010
 
5.9%
т 988
 
5.8%
р 893
 
5.2%
л 750
 
4.4%
в 715
 
4.2%
Other values (54) 6005
35.0%
Greek
ValueCountFrequency (%)
α 318
 
8.6%
τ 309
 
8.4%
ο 262
 
7.1%
ι 259
 
7.0%
ε 216
 
5.9%
ν 198
 
5.4%
σ 181
 
4.9%
η 168
 
4.6%
ρ 161
 
4.4%
π 135
 
3.7%
Other values (54) 1476
40.1%
Thai
ValueCountFrequency (%)
58
 
5.3%
57
 
5.2%
56
 
5.1%
53
 
4.8%
46
 
4.2%
44
 
4.0%
43
 
3.9%
43
 
3.9%
40
 
3.6%
37
 
3.4%
Other values (45) 624
56.7%
Arabic
ValueCountFrequency (%)
ا 171
14.6%
ر 92
 
7.8%
ی 89
 
7.6%
م 66
 
5.6%
و 64
 
5.5%
ل 61
 
5.2%
ن 58
 
4.9%
ه 56
 
4.8%
د 54
 
4.6%
ت 53
 
4.5%
Other values (34) 408
34.8%
Malayalam
ValueCountFrequency (%)
20
19.4%
8
 
7.8%
7
 
6.8%
6
 
5.8%
6
 
5.8%
5
 
4.9%
ി 3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (26) 39
37.9%
Hebrew
ValueCountFrequency (%)
א 10
13.3%
ל 8
 
10.7%
מ 6
 
8.0%
ו 6
 
8.0%
ש 4
 
5.3%
ה 4
 
5.3%
ר 4
 
5.3%
ב 3
 
4.0%
נ 3
 
4.0%
ם 3
 
4.0%
Other values (13) 24
32.0%
Oriya
ValueCountFrequency (%)
ି 6
15.0%
5
12.5%
3
 
7.5%
3
 
7.5%
2
 
5.0%
2
 
5.0%
2
 
5.0%
2
 
5.0%
2
 
5.0%
2
 
5.0%
Other values (11) 11
27.5%
Inherited
ValueCountFrequency (%)
20
30.8%
11
16.9%
̣ 8
 
12.3%
7
 
10.8%
́ 5
 
7.7%
̉ 4
 
6.2%
̃ 4
 
6.2%
̀ 2
 
3.1%
ّ 1
 
1.5%
ِ 1
 
1.5%
Other values (2) 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3176071
97.2%
None 18728
 
0.6%
Cyrillic 17139
 
0.5%
Katakana 14366
 
0.4%
CJK 14131
 
0.4%
Punctuation 13139
 
0.4%
Hiragana 6636
 
0.2%
Devanagari 2861
 
0.1%
Arabic 1182
 
< 0.1%
Thai 1101
 
< 0.1%
Other values (17) 1726
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
466715
14.7%
e 274258
 
8.6%
a 208839
 
6.6%
i 183804
 
5.8%
n 177349
 
5.6%
o 173652
 
5.5%
t 168184
 
5.3%
r 166093
 
5.2%
s 162424
 
5.1%
l 108147
 
3.4%
Other values (85) 1086606
34.2%
Punctuation
ValueCountFrequency (%)
7127
54.2%
2334
 
17.8%
1113
 
8.5%
869
 
6.6%
841
 
6.4%
489
 
3.7%
153
 
1.2%
138
 
1.1%
20
 
0.2%
15
 
0.1%
Other values (9) 40
 
0.3%
None
ValueCountFrequency (%)
é 1994
 
10.6%
á 921
 
4.9%
ó 774
 
4.1%
  769
 
4.1%
í 539
 
2.9%
ü 537
 
2.9%
ä 526
 
2.8%
508
 
2.7%
506
 
2.7%
450
 
2.4%
Other values (241) 11204
59.8%
Cyrillic
ValueCountFrequency (%)
о 1706
 
10.0%
и 1405
 
8.2%
а 1397
 
8.2%
е 1214
 
7.1%
н 1056
 
6.2%
с 1010
 
5.9%
т 988
 
5.8%
р 893
 
5.2%
л 750
 
4.4%
в 715
 
4.2%
Other values (54) 6005
35.0%
Katakana
ValueCountFrequency (%)
1527
 
10.6%
1034
 
7.2%
620
 
4.3%
600
 
4.2%
585
 
4.1%
485
 
3.4%
480
 
3.3%
426
 
3.0%
407
 
2.8%
386
 
2.7%
Other values (72) 7816
54.4%
Hiragana
ValueCountFrequency (%)
931
 
14.0%
523
 
7.9%
502
 
7.6%
458
 
6.9%
316
 
4.8%
260
 
3.9%
225
 
3.4%
210
 
3.2%
208
 
3.1%
206
 
3.1%
Other values (64) 2797
42.1%
CJK
ValueCountFrequency (%)
292
 
2.1%
265
 
1.9%
171
 
1.2%
160
 
1.1%
153
 
1.1%
147
 
1.0%
142
 
1.0%
139
 
1.0%
126
 
0.9%
126
 
0.9%
Other values (1793) 12410
87.8%
Devanagari
ValueCountFrequency (%)
283
 
9.9%
189
 
6.6%
183
 
6.4%
175
 
6.1%
152
 
5.3%
130
 
4.5%
118
 
4.1%
109
 
3.8%
109
 
3.8%
104
 
3.6%
Other values (55) 1309
45.8%
Arabic
ValueCountFrequency (%)
ا 171
14.5%
ر 92
 
7.8%
ی 89
 
7.5%
م 66
 
5.6%
و 64
 
5.4%
ل 61
 
5.2%
ن 58
 
4.9%
ه 56
 
4.7%
د 54
 
4.6%
ت 53
 
4.5%
Other values (40) 418
35.4%
Currency Symbols
ValueCountFrequency (%)
163
93.1%
12
 
6.9%
Thai
ValueCountFrequency (%)
58
 
5.3%
57
 
5.2%
56
 
5.1%
53
 
4.8%
46
 
4.2%
44
 
4.0%
43
 
3.9%
43
 
3.9%
40
 
3.6%
37
 
3.4%
Other values (45) 624
56.7%
Letterlike Symbols
ValueCountFrequency (%)
47
100.0%
Hangul
ValueCountFrequency (%)
38
 
4.6%
26
 
3.1%
24
 
2.9%
21
 
2.5%
18
 
2.2%
14
 
1.7%
14
 
1.7%
13
 
1.6%
12
 
1.4%
12
 
1.4%
Other values (220) 637
76.8%
Latin Ext Additional
ValueCountFrequency (%)
33
 
8.9%
28
 
7.5%
ế 21
 
5.7%
20
 
5.4%
20
 
5.4%
18
 
4.9%
16
 
4.3%
16
 
4.3%
15
 
4.0%
15
 
4.0%
Other values (28) 169
45.6%
Malayalam
ValueCountFrequency (%)
20
19.4%
8
 
7.8%
7
 
6.8%
6
 
5.8%
6
 
5.8%
5
 
4.9%
ി 3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (26) 39
37.9%
Hebrew
ValueCountFrequency (%)
א 10
13.3%
ל 8
 
10.7%
מ 6
 
8.0%
ו 6
 
8.0%
ש 4
 
5.3%
ה 4
 
5.3%
ר 4
 
5.3%
ב 3
 
4.0%
נ 3
 
4.0%
ם 3
 
4.0%
Other values (13) 24
32.0%
Math Operators
ValueCountFrequency (%)
8
57.1%
2
 
14.3%
2
 
14.3%
1
 
7.1%
1
 
7.1%
Diacriticals
ValueCountFrequency (%)
̣ 8
34.8%
́ 5
21.7%
̉ 4
17.4%
̃ 4
17.4%
̀ 2
 
8.7%
Box Drawing
ValueCountFrequency (%)
8
80.0%
2
 
20.0%
VS
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Oriya
ValueCountFrequency (%)
ି 6
15.0%
5
12.5%
3
 
7.5%
3
 
7.5%
2
 
5.0%
2
 
5.0%
2
 
5.0%
2
 
5.0%
2
 
5.0%
2
 
5.0%
Other values (11) 11
27.5%
Misc Symbols
ValueCountFrequency (%)
5
29.4%
4
23.5%
4
23.5%
3
17.6%
1
 
5.9%
Compat Jamo
ValueCountFrequency (%)
3
100.0%
Dingbats
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
1
 
20.0%
Arrows
ValueCountFrequency (%)
2
66.7%
1
33.3%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
Modifier Letters
ValueCountFrequency (%)
ʽ 1
50.0%
ʼ 1
50.0%
Distinct29677
Distinct (%)65.4%
Missing195
Missing (%)0.4%
Memory size711.8 KiB
2024-02-15T15:57:47.423747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length260
Median length250
Mean length195.86715
Min length3

Characters and Unicode

Total characters8884730
Distinct characters3774
Distinct categories23 ?
Distinct scripts15 ?
Distinct blocks30 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21575 ?
Unique (%)47.6%

Sample

1st rowUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon.
2nd row[株式会社Ainer] RANDEBOO(ランデブー)では2023年7月18日(火)より公式WEBストアにて2023 Autumn Winter コレクションの発売を開始。 シーズンコレクションとして、"Nepal Handmade ram vest"と”Nepal Handmade ram kn...
3rd rowUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon. "The rooftops of the world are…
4th rowEcologists caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday's disaster is a warning to take such caveats seriously
5th rowFrance lost 35 citizens, Thailand 33, US 31, Ukraine 21, Russia 19, UK 12, Nepal 10, Germany 10, Argentina 9, Canada 6, Romania 5, Portuguese 4, China 4, Philippines 4, Austria 4 The post 200 foreigners, dual nationals cut down in Hamas assault. Here’s where …
ValueCountFrequency (%)
the 66415
 
4.8%
of 30101
 
2.2%
a 29290
 
2.1%
to 28018
 
2.0%
and 27170
 
1.9%
in 26056
 
1.9%
on 13798
 
1.0%
for 12671
 
0.9%
is 11329
 
0.8%
with 9211
 
0.7%
Other values (108309) 1143258
81.8%
2024-02-15T15:57:48.566067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1352358
15.2%
e 800667
 
9.0%
a 573582
 
6.5%
t 524220
 
5.9%
i 498717
 
5.6%
n 494673
 
5.6%
o 478325
 
5.4%
r 445751
 
5.0%
s 431581
 
4.9%
l 275603
 
3.1%
Other values (3764) 3009253
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6487557
73.0%
Space Separator 1357042
 
15.3%
Uppercase Letter 480236
 
5.4%
Other Punctuation 215454
 
2.4%
Decimal Number 134538
 
1.5%
Other Letter 90478
 
1.0%
Dash Punctuation 31272
 
0.4%
Control 25076
 
0.3%
Open Punctuation 15536
 
0.2%
Close Punctuation 14870
 
0.2%
Other values (13) 32671
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1994
 
2.2%
1689
 
1.9%
1074
 
1.2%
1046
 
1.2%
1014
 
1.1%
990
 
1.1%
963
 
1.1%
929
 
1.0%
888
 
1.0%
856
 
0.9%
Other values (3149) 79035
87.4%
Lowercase Letter
ValueCountFrequency (%)
e 800667
12.3%
a 573582
 
8.8%
t 524220
 
8.1%
i 498717
 
7.7%
n 494673
 
7.6%
o 478325
 
7.4%
r 445751
 
6.9%
s 431581
 
6.7%
l 275603
 
4.2%
d 246473
 
3.8%
Other values (212) 1717965
26.5%
Uppercase Letter
ValueCountFrequency (%)
S 41001
 
8.5%
T 40731
 
8.5%
A 40568
 
8.4%
C 31751
 
6.6%
I 29157
 
6.1%
E 25403
 
5.3%
M 23792
 
5.0%
N 23592
 
4.9%
R 22873
 
4.8%
P 22002
 
4.6%
Other values (120) 179366
37.3%
Other Punctuation
ValueCountFrequency (%)
. 74857
34.7%
, 69202
32.1%
21113
 
9.8%
' 11972
 
5.6%
: 9064
 
4.2%
/ 7987
 
3.7%
" 5789
 
2.7%
% 3014
 
1.4%
? 2076
 
1.0%
1881
 
0.9%
Other values (32) 8499
 
3.9%
Nonspacing Mark
ValueCountFrequency (%)
468
22.0%
308
14.5%
246
11.6%
153
 
7.2%
105
 
4.9%
101
 
4.7%
98
 
4.6%
91
 
4.3%
86
 
4.0%
66
 
3.1%
Other values (26) 406
19.1%
Other Symbol
ValueCountFrequency (%)
® 97
23.8%
73
17.9%
© 52
12.8%
47
11.5%
° 33
 
8.1%
20
 
4.9%
18
 
4.4%
14
 
3.4%
6
 
1.5%
5
 
1.2%
Other values (25) 42
10.3%
Decimal Number
ValueCountFrequency (%)
2 32108
23.9%
0 27221
20.2%
1 19618
14.6%
3 15002
11.2%
5 8574
 
6.4%
4 8347
 
6.2%
9 6352
 
4.7%
6 5944
 
4.4%
8 5731
 
4.3%
7 5557
 
4.1%
Other values (16) 84
 
0.1%
Spacing Mark
ValueCountFrequency (%)
689
41.4%
346
20.8%
ि 282
16.9%
205
 
12.3%
ി 28
 
1.7%
19
 
1.1%
18
 
1.1%
14
 
0.8%
13
 
0.8%
11
 
0.7%
Other values (9) 39
 
2.3%
Math Symbol
ValueCountFrequency (%)
< 1042
30.7%
> 1036
30.6%
+ 761
22.4%
| 199
 
5.9%
= 110
 
3.2%
76
 
2.2%
~ 33
 
1.0%
28
 
0.8%
× 24
 
0.7%
23
 
0.7%
Other values (8) 59
 
1.7%
Open Punctuation
ValueCountFrequency (%)
( 10922
70.3%
[ 2585
 
16.6%
786
 
5.1%
637
 
4.1%
247
 
1.6%
182
 
1.2%
91
 
0.6%
{ 40
 
0.3%
36
 
0.2%
5
 
< 0.1%
Other values (3) 5
 
< 0.1%
Control
ValueCountFrequency (%)
24347
97.1%
650
 
2.6%
€ 41
 
0.2%
™ 24
 
0.1%
 3
 
< 0.1%
œ 3
 
< 0.1%
• 2
 
< 0.1%
” 2
 
< 0.1%
’ 1
 
< 0.1%
“ 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10720
72.1%
] 2513
 
16.9%
721
 
4.8%
591
 
4.0%
169
 
1.1%
88
 
0.6%
38
 
0.3%
} 20
 
0.1%
5
 
< 0.1%
3
 
< 0.1%
Other values (2) 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1352358
99.7%
  4475
 
0.3%
  189
 
< 0.1%
10
 
< 0.1%
6
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
2545
98.6%
24
 
0.9%
4
 
0.2%
3
 
0.1%
ـ 3
 
0.1%
2
 
0.1%
ˈ 1
 
< 0.1%
Format
ValueCountFrequency (%)
69
43.4%
44
27.7%
24
 
15.1%
 9
 
5.7%
­ 5
 
3.1%
4
 
2.5%
4
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 27059
86.5%
2807
 
9.0%
1391
 
4.4%
7
 
< 0.1%
6
 
< 0.1%
2
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 3808
91.6%
216
 
5.2%
£ 87
 
2.1%
43
 
1.0%
¥ 1
 
< 0.1%
¢ 1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
11092
80.3%
2437
 
17.6%
» 265
 
1.9%
22
 
0.2%
Modifier Symbol
ValueCountFrequency (%)
^ 20
51.3%
` 12
30.8%
4
 
10.3%
´ 3
 
7.7%
Initial Punctuation
ValueCountFrequency (%)
3066
74.6%
756
 
18.4%
« 288
 
7.0%
Other Number
ValueCountFrequency (%)
² 12
70.6%
½ 4
 
23.5%
1
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 200
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6911788
77.8%
Common 1822499
 
20.5%
Cyrillic 45840
 
0.5%
Han 36460
 
0.4%
Katakana 20825
 
0.2%
Hiragana 18127
 
0.2%
Greek 10205
 
0.1%
Devanagari 7320
 
0.1%
Hangul 3922
 
< 0.1%
Arabic 3625
 
< 0.1%
Other values (5) 4119
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
856
 
2.3%
641
 
1.8%
585
 
1.6%
573
 
1.6%
504
 
1.4%
488
 
1.3%
436
 
1.2%
405
 
1.1%
400
 
1.1%
387
 
1.1%
Other values (2348) 31185
85.5%
Hangul
ValueCountFrequency (%)
102
 
2.6%
93
 
2.4%
83
 
2.1%
70
 
1.8%
68
 
1.7%
67
 
1.7%
57
 
1.5%
57
 
1.5%
57
 
1.5%
54
 
1.4%
Other values (398) 3214
81.9%
Latin
ValueCountFrequency (%)
e 800667
 
11.6%
a 573582
 
8.3%
t 524220
 
7.6%
i 498717
 
7.2%
n 494673
 
7.2%
o 478325
 
6.9%
r 445751
 
6.4%
s 431581
 
6.2%
l 275603
 
4.0%
d 246473
 
3.6%
Other values (214) 2142196
31.0%
Common
ValueCountFrequency (%)
1352358
74.2%
. 74857
 
4.1%
, 69202
 
3.8%
2 32108
 
1.8%
0 27221
 
1.5%
- 27059
 
1.5%
24347
 
1.3%
21113
 
1.2%
1 19618
 
1.1%
3 15002
 
0.8%
Other values (177) 159614
 
8.8%
Katakana
ValueCountFrequency (%)
1689
 
8.1%
990
 
4.8%
963
 
4.6%
929
 
4.5%
832
 
4.0%
748
 
3.6%
680
 
3.3%
657
 
3.2%
625
 
3.0%
622
 
3.0%
Other values (76) 12090
58.1%
Hiragana
ValueCountFrequency (%)
1994
 
11.0%
1074
 
5.9%
1046
 
5.8%
1014
 
5.6%
888
 
4.9%
841
 
4.6%
834
 
4.6%
814
 
4.5%
765
 
4.2%
700
 
3.9%
Other values (65) 8157
45.0%
Cyrillic
ValueCountFrequency (%)
о 4691
 
10.2%
и 3743
 
8.2%
а 3688
 
8.0%
е 3587
 
7.8%
н 3013
 
6.6%
т 2748
 
6.0%
с 2584
 
5.6%
р 2481
 
5.4%
в 1909
 
4.2%
л 1881
 
4.1%
Other values (57) 15515
33.8%
Greek
ValueCountFrequency (%)
α 940
 
9.2%
τ 823
 
8.1%
ο 752
 
7.4%
ι 717
 
7.0%
ε 626
 
6.1%
ν 567
 
5.6%
σ 483
 
4.7%
ρ 450
 
4.4%
η 401
 
3.9%
κ 387
 
3.8%
Other values (54) 4059
39.8%
Devanagari
ValueCountFrequency (%)
689
 
9.4%
482
 
6.6%
471
 
6.4%
468
 
6.4%
346
 
4.7%
308
 
4.2%
305
 
4.2%
289
 
3.9%
ि 282
 
3.9%
268
 
3.7%
Other values (52) 3412
46.6%
Thai
ValueCountFrequency (%)
174
 
5.7%
170
 
5.6%
164
 
5.4%
153
 
5.0%
152
 
5.0%
131
 
4.3%
129
 
4.2%
112
 
3.7%
107
 
3.5%
105
 
3.4%
Other values (52) 1648
54.1%
Arabic
ValueCountFrequency (%)
ا 511
14.1%
ی 268
 
7.4%
م 236
 
6.5%
ر 226
 
6.2%
ن 220
 
6.1%
ل 212
 
5.8%
و 207
 
5.7%
د 203
 
5.6%
ه 173
 
4.8%
ت 171
 
4.7%
Other values (46) 1198
33.0%
Malayalam
ValueCountFrequency (%)
60
 
14.6%
ി 28
 
6.8%
28
 
6.8%
26
 
6.3%
23
 
5.6%
20
 
4.9%
18
 
4.4%
18
 
4.4%
15
 
3.6%
13
 
3.2%
Other values (41) 162
39.4%
Oriya
ValueCountFrequency (%)
ି 15
 
8.7%
14
 
8.1%
11
 
6.4%
11
 
6.4%
10
 
5.8%
9
 
5.2%
9
 
5.2%
9
 
5.2%
6
 
3.5%
6
 
3.5%
Other values (28) 73
42.2%
Hebrew
ValueCountFrequency (%)
ו 35
10.6%
י 34
10.3%
ה 28
 
8.5%
ר 26
 
7.9%
מ 25
 
7.6%
א 25
 
7.6%
ש 22
 
6.6%
ל 20
 
6.0%
ב 18
 
5.4%
ח 16
 
4.8%
Other values (13) 82
24.8%
Inherited
ValueCountFrequency (%)
69
43.4%
24
 
15.1%
19
 
11.9%
́ 13
 
8.2%
̣ 10
 
6.3%
̀ 8
 
5.0%
̂ 5
 
3.1%
ّ 3
 
1.9%
ُ 2
 
1.3%
ً 2
 
1.3%
Other values (3) 4
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8646544
97.3%
None 50013
 
0.6%
Cyrillic 45840
 
0.5%
Punctuation 43271
 
0.5%
CJK 36424
 
0.4%
Katakana 23846
 
0.3%
Hiragana 18131
 
0.2%
Devanagari 7347
 
0.1%
Hangul 3922
 
< 0.1%
Arabic 3635
 
< 0.1%
Other values (20) 5757
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1352358
15.6%
e 800667
 
9.3%
a 573582
 
6.6%
t 524220
 
6.1%
i 498717
 
5.8%
n 494673
 
5.7%
o 478325
 
5.5%
r 445751
 
5.2%
s 431581
 
5.0%
l 275603
 
3.2%
Other values (87) 2771067
32.0%
Punctuation
ValueCountFrequency (%)
21113
48.8%
11092
25.6%
3066
 
7.1%
2807
 
6.5%
2437
 
5.6%
1391
 
3.2%
756
 
1.7%
247
 
0.6%
115
 
0.3%
69
 
0.2%
Other values (14) 178
 
0.4%
None
ValueCountFrequency (%)
é 5463
 
10.9%
  4475
 
8.9%
ó 2107
 
4.2%
á 2094
 
4.2%
1881
 
3.8%
ä 1542
 
3.1%
í 1473
 
2.9%
ü 1432
 
2.9%
1028
 
2.1%
ö 1023
 
2.0%
Other values (277) 27495
55.0%
Cyrillic
ValueCountFrequency (%)
о 4691
 
10.2%
и 3743
 
8.2%
а 3688
 
8.0%
е 3587
 
7.8%
н 3013
 
6.6%
т 2748
 
6.0%
с 2584
 
5.6%
р 2481
 
5.4%
в 1909
 
4.2%
л 1881
 
4.1%
Other values (57) 15515
33.8%
Katakana
ValueCountFrequency (%)
2545
 
10.7%
1689
 
7.1%
990
 
4.2%
963
 
4.0%
929
 
3.9%
832
 
3.5%
748
 
3.1%
680
 
2.9%
657
 
2.8%
625
 
2.6%
Other values (74) 13188
55.3%
Hiragana
ValueCountFrequency (%)
1994
 
11.0%
1074
 
5.9%
1046
 
5.8%
1014
 
5.6%
888
 
4.9%
841
 
4.6%
834
 
4.6%
814
 
4.5%
765
 
4.2%
700
 
3.9%
Other values (66) 8161
45.0%
CJK
ValueCountFrequency (%)
856
 
2.4%
641
 
1.8%
585
 
1.6%
573
 
1.6%
504
 
1.4%
488
 
1.3%
436
 
1.2%
405
 
1.1%
400
 
1.1%
387
 
1.1%
Other values (2338) 31149
85.5%
Devanagari
ValueCountFrequency (%)
689
 
9.4%
482
 
6.6%
471
 
6.4%
468
 
6.4%
346
 
4.7%
308
 
4.2%
305
 
4.2%
289
 
3.9%
ि 282
 
3.8%
268
 
3.6%
Other values (53) 3439
46.8%
Arabic
ValueCountFrequency (%)
ا 511
14.1%
ی 268
 
7.4%
م 236
 
6.5%
ر 226
 
6.2%
ن 220
 
6.1%
ل 212
 
5.8%
و 207
 
5.7%
د 203
 
5.6%
ه 173
 
4.8%
ت 171
 
4.7%
Other values (43) 1208
33.2%
Currency Symbols
ValueCountFrequency (%)
216
83.4%
43
 
16.6%
Thai
ValueCountFrequency (%)
174
 
5.7%
170
 
5.6%
164
 
5.4%
153
 
5.0%
152
 
5.0%
131
 
4.3%
129
 
4.2%
112
 
3.7%
107
 
3.5%
105
 
3.4%
Other values (52) 1648
54.1%
Hangul
ValueCountFrequency (%)
102
 
2.6%
93
 
2.4%
83
 
2.1%
70
 
1.8%
68
 
1.7%
67
 
1.7%
57
 
1.5%
57
 
1.5%
57
 
1.5%
54
 
1.4%
Other values (398) 3214
81.9%
Latin Ext Additional
ValueCountFrequency (%)
98
 
8.0%
ế 95
 
7.8%
85
 
7.0%
78
 
6.4%
66
 
5.4%
62
 
5.1%
56
 
4.6%
52
 
4.3%
51
 
4.2%
49
 
4.0%
Other values (35) 526
43.2%
Letterlike Symbols
ValueCountFrequency (%)
73
98.6%
1
 
1.4%
Malayalam
ValueCountFrequency (%)
60
 
14.6%
ി 28
 
6.8%
28
 
6.8%
26
 
6.3%
23
 
5.6%
20
 
4.9%
18
 
4.4%
18
 
4.4%
15
 
3.6%
13
 
3.2%
Other values (41) 162
39.4%
Geometric Shapes
ValueCountFrequency (%)
47
40.9%
20
17.4%
18
 
15.7%
14
 
12.2%
6
 
5.2%
2
 
1.7%
2
 
1.7%
2
 
1.7%
2
 
1.7%
1
 
0.9%
Hebrew
ValueCountFrequency (%)
ו 35
10.6%
י 34
10.3%
ה 28
 
8.5%
ר 26
 
7.9%
מ 25
 
7.6%
א 25
 
7.6%
ש 22
 
6.6%
ל 20
 
6.0%
ב 18
 
5.4%
ח 16
 
4.8%
Other values (13) 82
24.8%
Arrows
ValueCountFrequency (%)
28
84.8%
4
 
12.1%
1
 
3.0%
VS
ValueCountFrequency (%)
19
95.0%
1
 
5.0%
Oriya
ValueCountFrequency (%)
ି 15
 
8.7%
14
 
8.1%
11
 
6.4%
11
 
6.4%
10
 
5.8%
9
 
5.2%
9
 
5.2%
9
 
5.2%
6
 
3.5%
6
 
3.5%
Other values (28) 73
42.2%
Diacriticals
ValueCountFrequency (%)
́ 13
33.3%
̣ 10
25.6%
̀ 8
20.5%
̂ 5
 
12.8%
̉ 2
 
5.1%
̃ 1
 
2.6%
Misc Symbols
ValueCountFrequency (%)
5
62.5%
2
 
25.0%
1
 
12.5%
Dingbats
ValueCountFrequency (%)
4
28.6%
4
28.6%
2
14.3%
2
14.3%
1
 
7.1%
1
 
7.1%
CJK Radicals Sup
ValueCountFrequency (%)
2
100.0%
Kangxi
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Phonetic Ext
ValueCountFrequency (%)
2
100.0%
Block Elements
ValueCountFrequency (%)
2
100.0%
IPA Ext
ValueCountFrequency (%)
ɛ 1
100.0%
Modifier Letters
ValueCountFrequency (%)
ˈ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
100.0%

url
Text

Distinct30688
Distinct (%)67.4%
Missing14
Missing (%)< 0.1%
Memory size711.8 KiB
2024-02-15T15:57:48.972212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length439
Median length225
Mean length99.683633
Min length19

Characters and Unicode

Total characters4539792
Distinct characters139
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22844 ?
Unique (%)50.2%

Sample

1st rowhttps://www.ibtimes.com/un-chief-urges-world-stop-madness-climate-change-3716939
2nd rowhttps://prtimes.jp/main/html/rd/p/000000147.000032220.html
3rd rowhttps://www.voanews.com/a/un-chief-urges-world-to-stop-the-madness-of-climate-change-/7332605.html
4th rowhttps://indianexpress.com/article/opinion/editorials/sikkim-warning-hydroelectricity-push-must-be-accompanied-by-safety-measures-8970365/
5th rowhttps://www.timesofisrael.com/200-foreigners-dual-nationals-cut-down-in-hamas-assault-heres-where-they-were-from/
ValueCountFrequency (%)
https://removed.com 834
 
1.8%
https://www.iphoneincanada.ca/2023/11/21/freedom-mobile-roam-beyond 26
 
0.1%
https://time.com/6337364/top-100-photos-2023 26
 
0.1%
https://www.bitpipe.com/detail/res/1700486941_181.html 26
 
0.1%
https://loyaltylobby.com/2023/11/21/qatar-airways-buy-avios-50-bonus-through-november-27-2023 22
 
< 0.1%
https://www.gamesindustry.biz/assassins-creed-spider-man-mario-and-ps5-boost-sales-in-europe-during-october-european-monthly-charts 21
 
< 0.1%
https://skift.com/2023/11/20/vietnam-looks-to-offer-visa-free-entry-to-indians-india-report 20
 
< 0.1%
https://deadline.com/2023/11/2023-ida-documentary-awards-nominations-announcement-1235630865 20
 
< 0.1%
https://blog.frequentflyerbonuses.com/2023/11/best-western-rewards-100-travel-card.html 20
 
< 0.1%
https://travelingformiles.com/qatar-airways-black-friday-sale-up-to-10000-bonus-avios-offer-europe 19
 
< 0.1%
Other values (30680) 44510
97.7%
2024-02-15T15:57:50.028194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 374357
 
8.2%
e 337422
 
7.4%
t 299547
 
6.6%
s 264034
 
5.8%
/ 260370
 
5.7%
a 242719
 
5.3%
o 232317
 
5.1%
i 216450
 
4.8%
n 211948
 
4.7%
r 196646
 
4.3%
Other values (129) 1903982
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3266569
72.0%
Decimal Number 403533
 
8.9%
Other Punctuation 401897
 
8.9%
Dash Punctuation 374357
 
8.2%
Uppercase Letter 87739
 
1.9%
Connector Punctuation 4245
 
0.1%
Math Symbol 966
 
< 0.1%
Other Letter 424
 
< 0.1%
Control 28
 
< 0.1%
Space Separator 17
 
< 0.1%
Other values (5) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 63
14.9%
ی 49
11.6%
ر 39
 
9.2%
ت 27
 
6.4%
ه 26
 
6.1%
ن 25
 
5.9%
و 24
 
5.7%
ب 22
 
5.2%
د 20
 
4.7%
م 19
 
4.5%
Other values (23) 110
25.9%
Uppercase Letter
ValueCountFrequency (%)
A 8312
 
9.5%
S 7540
 
8.6%
E 6192
 
7.1%
R 5821
 
6.6%
I 5637
 
6.4%
C 5443
 
6.2%
T 4572
 
5.2%
N 4433
 
5.1%
M 4225
 
4.8%
O 4080
 
4.7%
Other values (20) 31484
35.9%
Lowercase Letter
ValueCountFrequency (%)
e 337422
 
10.3%
t 299547
 
9.2%
s 264034
 
8.1%
a 242719
 
7.4%
o 232317
 
7.1%
i 216450
 
6.6%
n 211948
 
6.5%
r 196646
 
6.0%
c 151028
 
4.6%
w 140640
 
4.3%
Other values (19) 973818
29.8%
Other Punctuation
ValueCountFrequency (%)
/ 260370
64.8%
. 91789
 
22.8%
: 45569
 
11.3%
% 2829
 
0.7%
? 565
 
0.1%
& 286
 
0.1%
; 180
 
< 0.1%
, 158
 
< 0.1%
# 132
 
< 0.1%
! 15
 
< 0.1%
Other values (2) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 85558
21.2%
1 73848
18.3%
0 62626
15.5%
3 45247
11.2%
4 27313
 
6.8%
5 24265
 
6.0%
7 22099
 
5.5%
8 21690
 
5.4%
9 20624
 
5.1%
6 20263
 
5.0%
Control
ValueCountFrequency (%)
Œ 6
21.4%
5
17.9%
5
17.9%
† 5
17.9%
‡ 2
 
7.1%
€ 2
 
7.1%
… 2
 
7.1%
ˆ 1
 
3.6%
Math Symbol
ValueCountFrequency (%)
= 880
91.1%
~ 56
 
5.8%
+ 23
 
2.4%
± 3
 
0.3%
3
 
0.3%
¬ 1
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
¯ 5
55.6%
¨ 2
 
22.2%
´ 2
 
22.2%
Other Symbol
ValueCountFrequency (%)
° 2
66.7%
© 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 374357
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4245
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Number
ValueCountFrequency (%)
² 3
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 1
100.0%
Format
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3354308
73.9%
Common 1185061
 
26.1%
Arabic 422
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 337422
 
10.1%
t 299547
 
8.9%
s 264034
 
7.9%
a 242719
 
7.2%
o 232317
 
6.9%
i 216450
 
6.5%
n 211948
 
6.3%
r 196646
 
5.9%
c 151028
 
4.5%
w 140640
 
4.2%
Other values (49) 1061557
31.6%
Common
ValueCountFrequency (%)
- 374357
31.6%
/ 260370
22.0%
. 91789
 
7.7%
2 85558
 
7.2%
1 73848
 
6.2%
0 62626
 
5.3%
: 45569
 
3.8%
3 45247
 
3.8%
4 27313
 
2.3%
5 24265
 
2.0%
Other values (37) 94119
 
7.9%
Arabic
ValueCountFrequency (%)
ا 63
14.9%
ی 49
11.6%
ر 39
 
9.2%
ت 27
 
6.4%
ه 26
 
6.2%
ن 25
 
5.9%
و 24
 
5.7%
ب 22
 
5.2%
د 20
 
4.7%
م 19
 
4.5%
Other values (22) 108
25.6%
Inherited
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4539277
> 99.9%
Arabic 422
 
< 0.1%
None 89
 
< 0.1%
Math Operators 3
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 374357
 
8.2%
e 337422
 
7.4%
t 299547
 
6.6%
s 264034
 
5.8%
/ 260370
 
5.7%
a 242719
 
5.3%
o 232317
 
5.1%
i 216450
 
4.8%
n 211948
 
4.7%
r 196646
 
4.3%
Other values (71) 1903467
41.9%
Arabic
ValueCountFrequency (%)
ا 63
14.9%
ی 49
11.6%
ر 39
 
9.2%
ت 27
 
6.4%
ه 26
 
6.2%
ن 25
 
5.9%
و 24
 
5.7%
ب 22
 
5.2%
د 20
 
4.7%
م 19
 
4.5%
Other values (22) 108
25.6%
None
ValueCountFrequency (%)
Ø 22
24.7%
Ù 9
 
10.1%
Û 7
 
7.9%
Œ 6
 
6.7%
¯ 5
 
5.6%
† 5
 
5.6%
± 3
 
3.4%
² 3
 
3.4%
Ú 3
 
3.4%
§ 3
 
3.4%
Other values (14) 23
25.8%
Math Operators
ValueCountFrequency (%)
3
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

url_to_image
Text

MISSING 

Distinct25345
Distinct (%)59.3%
Missing2826
Missing (%)6.2%
Memory size711.8 KiB
2024-02-15T15:57:50.691960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1105
Median length324
Mean length105.04535
Min length25

Characters and Unicode

Total characters4488588
Distinct characters228
Distinct categories16 ?
Distinct scripts10 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18436 ?
Unique (%)43.1%

Sample

1st rowhttps://d.ibtimes.com/en/full/4496078/nepals-glaciers-melted-65-percent-faster-last-decade-previous-one-said-guterres-who.jpg
2nd rowhttps://prtimes.jp/i/32220/147/ogp/d32220-147-21916971e381da87f7a9-0.jpg
3rd rowhttps://gdb.voanews.com/01000000-0a00-0242-60fd-08dbd93618b1_w1200_r1.jpg
4th rowhttps://images.indianexpress.com/2023/10/edit-4.jpg
5th rowhttps://static.timesofisrael.com/www/uploads/2023/10/231011160737-emily-hand-beeri-victim.jpg
ValueCountFrequency (%)
https://www.marketscreener.com/images/twitter_ms_fdnoir.png 939
 
2.2%
https://www.marketscreener.com/images/twitter_ms_fdblanc.png 871
 
2.0%
https://cdn08.allafrica.com/static/images/structure/aa-logo-rgba-no-text-square.png 350
 
0.8%
https://s3.amazonaws.com/images.investorsobserver.com/io-logo-800x450.jpg 210
 
0.5%
https://ml.globenewswire.com/resource/download/b27a94e8-407e-46ae-a5c2-47bb2a1c8a2e 200
 
0.5%
https://ml.globenewswire.com/resource/download/908fb457-7f8e-4a08-9081-5565e3dfb3d7 163
 
0.4%
https://sportshub.cbsistatic.com/i/r/2019/11/06/f93a1691-1afb-4ff8-bc0e-06a49a5126f9/thumbnail/1200x675/cbb0b742977fbfc4f6ee3839bfe83e44/college-basketball-rim.jpg 125
 
0.3%
https://ml.globenewswire.com/resource/download/8825ef83-644c-4c64-a208-863167237797 119
 
0.3%
https://politicalwire.com/wp-content/uploads/2018/02/pw-podcast-logo.jpg 113
 
0.3%
https://ml.globenewswire.com/resource/download/ced67b94-5d6b-4532-b365-e1f1f26f8f16 109
 
0.3%
Other values (25410) 39657
92.5%
2024-02-15T15:57:51.830555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 292670
 
6.5%
t 252433
 
5.6%
e 237993
 
5.3%
s 193173
 
4.3%
o 182977
 
4.1%
a 180438
 
4.0%
p 164093
 
3.7%
i 159819
 
3.6%
c 158991
 
3.5%
0 152961
 
3.4%
Other values (218) 2513040
56.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2722491
60.7%
Decimal Number 852181
 
19.0%
Other Punctuation 543551
 
12.1%
Uppercase Letter 147900
 
3.3%
Dash Punctuation 139312
 
3.1%
Connector Punctuation 46423
 
1.0%
Math Symbol 34074
 
0.8%
Close Punctuation 1187
 
< 0.1%
Open Punctuation 1187
 
< 0.1%
Space Separator 126
 
< 0.1%
Other values (6) 156
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
4.0%
5
 
4.0%
5
 
4.0%
5
 
4.0%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (72) 86
69.4%
Lowercase Letter
ValueCountFrequency (%)
t 252433
 
9.3%
e 237993
 
8.7%
s 193173
 
7.1%
o 182977
 
6.7%
a 180438
 
6.6%
p 164093
 
6.0%
i 159819
 
5.9%
c 158991
 
5.8%
n 131682
 
4.8%
m 119893
 
4.4%
Other values (46) 940999
34.6%
Uppercase Letter
ValueCountFrequency (%)
A 10029
 
6.8%
C 9239
 
6.2%
F 8951
 
6.1%
S 8855
 
6.0%
M 8166
 
5.5%
R 7742
 
5.2%
D 7582
 
5.1%
P 7376
 
5.0%
T 6976
 
4.7%
E 6731
 
4.6%
Other values (28) 66253
44.8%
Other Punctuation
ValueCountFrequency (%)
/ 292670
53.8%
. 141768
26.1%
: 49132
 
9.0%
& 19078
 
3.5%
, 16442
 
3.0%
? 12821
 
2.4%
% 10603
 
2.0%
! 430
 
0.1%
; 174
 
< 0.1%
" 168
 
< 0.1%
Other values (6) 265
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 152961
17.9%
2 137747
16.2%
1 129942
15.2%
3 80972
9.5%
4 65328
7.7%
6 64475
7.6%
5 61166
 
7.2%
7 53829
 
6.3%
8 53513
 
6.3%
9 52248
 
6.1%
Math Symbol
ValueCountFrequency (%)
= 33470
98.2%
+ 560
 
1.6%
~ 33
 
0.1%
× 7
 
< 0.1%
| 4
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1128
95.0%
] 52
 
4.4%
} 6
 
0.5%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1128
95.0%
[ 52
 
4.4%
{ 6
 
0.5%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 139302
> 99.9%
9
 
< 0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
105
83.3%
21
 
16.7%
Nonspacing Mark
ValueCountFrequency (%)
3
50.0%
́ 3
50.0%
Other Symbol
ValueCountFrequency (%)
3
75.0%
© 1
 
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 46423
100.0%
Initial Punctuation
ValueCountFrequency (%)
17
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2869913
63.9%
Common 1618067
36.0%
Cyrillic 474
 
< 0.1%
Han 52
 
< 0.1%
Hangul 32
 
< 0.1%
Katakana 24
 
< 0.1%
Hebrew 11
 
< 0.1%
Inherited 6
 
< 0.1%
Hiragana 5
 
< 0.1%
Greek 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 252433
 
8.8%
e 237993
 
8.3%
s 193173
 
6.7%
o 182977
 
6.4%
a 180438
 
6.3%
p 164093
 
5.7%
i 159819
 
5.6%
c 158991
 
5.5%
n 131682
 
4.6%
m 119893
 
4.2%
Other values (46) 1088421
37.9%
Common
ValueCountFrequency (%)
/ 292670
18.1%
0 152961
9.5%
. 141768
8.8%
- 139302
 
8.6%
2 137747
 
8.5%
1 129942
 
8.0%
3 80972
 
5.0%
4 65328
 
4.0%
6 64475
 
4.0%
5 61166
 
3.8%
Other values (40) 351736
21.7%
Cyrillic
ValueCountFrequency (%)
е 66
13.9%
а 59
12.4%
н 43
 
9.1%
в 40
 
8.4%
с 38
 
8.0%
р 29
 
6.1%
и 27
 
5.7%
о 15
 
3.2%
т 15
 
3.2%
ь 14
 
3.0%
Other values (24) 128
27.0%
Han
ValueCountFrequency (%)
5
 
9.6%
5
 
9.6%
5
 
9.6%
5
 
9.6%
2
 
3.8%
2
 
3.8%
2
 
3.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
Other values (23) 23
44.2%
Hangul
ValueCountFrequency (%)
3
 
9.4%
2
 
6.2%
2
 
6.2%
2
 
6.2%
2
 
6.2%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
Other values (16) 16
50.0%
Hebrew
ValueCountFrequency (%)
ל 2
18.2%
ס 1
9.1%
ז 1
9.1%
ה 1
9.1%
ק 1
9.1%
ב 1
9.1%
ח 1
9.1%
א 1
9.1%
מ 1
9.1%
ן 1
9.1%
Katakana
ValueCountFrequency (%)
3
12.5%
3
12.5%
3
12.5%
3
12.5%
3
12.5%
3
12.5%
3
12.5%
3
12.5%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Greek
ValueCountFrequency (%)
Τ 1
25.0%
Ε 1
25.0%
Π 1
25.0%
Α 1
25.0%
Inherited
ValueCountFrequency (%)
3
50.0%
́ 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4487894
> 99.9%
Cyrillic 474
 
< 0.1%
CJK 52
 
< 0.1%
Punctuation 50
 
< 0.1%
None 34
 
< 0.1%
Hangul 32
 
< 0.1%
Katakana 27
 
< 0.1%
Hebrew 11
 
< 0.1%
Hiragana 5
 
< 0.1%
VS 3
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 292670
 
6.5%
t 252433
 
5.6%
e 237993
 
5.3%
s 193173
 
4.3%
o 182977
 
4.1%
a 180438
 
4.0%
p 164093
 
3.7%
i 159819
 
3.6%
c 158991
 
3.5%
0 152961
 
3.4%
Other values (80) 2512346
56.0%
Cyrillic
ValueCountFrequency (%)
е 66
13.9%
а 59
12.4%
н 43
 
9.1%
в 40
 
8.4%
с 38
 
8.0%
р 29
 
6.1%
и 27
 
5.7%
о 15
 
3.2%
т 15
 
3.2%
ь 14
 
3.0%
Other values (24) 128
27.0%
Punctuation
ValueCountFrequency (%)
21
42.0%
17
34.0%
9
18.0%
2
 
4.0%
1
 
2.0%
None
ValueCountFrequency (%)
· 14
41.2%
× 7
20.6%
í 2
 
5.9%
ü 2
 
5.9%
ú 1
 
2.9%
1
 
2.9%
Τ 1
 
2.9%
ä 1
 
2.9%
Ε 1
 
2.9%
Π 1
 
2.9%
Other values (3) 3
 
8.8%
CJK
ValueCountFrequency (%)
5
 
9.6%
5
 
9.6%
5
 
9.6%
5
 
9.6%
2
 
3.8%
2
 
3.8%
2
 
3.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
Other values (23) 23
44.2%
Katakana
ValueCountFrequency (%)
3
11.1%
3
11.1%
3
11.1%
3
11.1%
3
11.1%
3
11.1%
3
11.1%
3
11.1%
3
11.1%
VS
ValueCountFrequency (%)
3
100.0%
Hangul
ValueCountFrequency (%)
3
 
9.4%
2
 
6.2%
2
 
6.2%
2
 
6.2%
2
 
6.2%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
Other values (16) 16
50.0%
Specials
ValueCountFrequency (%)
3
100.0%
Diacriticals
ValueCountFrequency (%)
́ 3
100.0%
Hebrew
ValueCountFrequency (%)
ל 2
18.2%
ס 1
9.1%
ז 1
9.1%
ה 1
9.1%
ק 1
9.1%
ב 1
9.1%
ח 1
9.1%
א 1
9.1%
מ 1
9.1%
ן 1
9.1%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Distinct23427
Distinct (%)51.4%
Missing16
Missing (%)< 0.1%
Memory size711.8 KiB
2024-02-15T15:57:52.576392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length26
Mean length25.998551
Min length2

Characters and Unicode

Total characters1183974
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15531 ?
Unique (%)34.1%

Sample

1st row2023-10-30 10:12:35.000000
2nd row2023-10-06 04:40:02.000000
3rd row2023-10-30 10:53:30.000000
4th row2023-10-06 01:20:24.000000
5th row2023-10-27 01:08:34.000000
ValueCountFrequency (%)
2023-11-21 23603
25.9%
2023-11-02 8603
 
9.4%
2023-11-22 8131
 
8.9%
00:00:00.000000 1134
 
1.2%
1970-01-01 834
 
0.9%
12:00:00.000000 378
 
0.4%
06:00:00.000000 346
 
0.4%
14:00:00.000000 340
 
0.4%
11:00:00.000000 275
 
0.3%
10:00:00.000000 255
 
0.3%
Other values (19866) 47180
51.8%
2024-02-15T15:57:53.882510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 419046
35.4%
1 164791
 
13.9%
2 163501
 
13.8%
- 91078
 
7.7%
: 91078
 
7.7%
3 68454
 
5.8%
45539
 
3.8%
. 45533
 
3.8%
4 24796
 
2.1%
5 23877
 
2.0%
Other values (4) 46281
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 910746
76.9%
Other Punctuation 136611
 
11.5%
Dash Punctuation 91078
 
7.7%
Space Separator 45539
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 419046
46.0%
1 164791
 
18.1%
2 163501
 
18.0%
3 68454
 
7.5%
4 24796
 
2.7%
5 23877
 
2.6%
6 13223
 
1.5%
7 12027
 
1.3%
8 10762
 
1.2%
9 10269
 
1.1%
Other Punctuation
ValueCountFrequency (%)
: 91078
66.7%
. 45533
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 91078
100.0%
Space Separator
ValueCountFrequency (%)
45539
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1183974
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 419046
35.4%
1 164791
 
13.9%
2 163501
 
13.8%
- 91078
 
7.7%
: 91078
 
7.7%
3 68454
 
5.8%
45539
 
3.8%
. 45533
 
3.8%
4 24796
 
2.1%
5 23877
 
2.0%
Other values (4) 46281
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1183974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 419046
35.4%
1 164791
 
13.9%
2 163501
 
13.8%
- 91078
 
7.7%
: 91078
 
7.7%
3 68454
 
5.8%
45539
 
3.8%
. 45533
 
3.8%
4 24796
 
2.1%
5 23877
 
2.0%
Other values (4) 46281
 
3.9%
Distinct29267
Distinct (%)64.3%
Missing35
Missing (%)0.1%
Memory size711.8 KiB
2024-02-15T15:57:54.834318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length216
Median length214
Mean length207.49379
Min length9

Characters and Unicode

Total characters9445325
Distinct characters688
Distinct categories22 ?
Distinct scripts9 ?
Distinct blocks18 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21260 ?
Unique (%)46.7%

Sample

1st rowUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastatin… [+2444 chars]
2nd rowRANDEBOO2023718()WEB2023 Autumn Winter "Nepal Handmade ram vest"Nepal Handmade ram knit"2023106()12:00WEB 106() 12:00 https://x.gd/226uY Nepal Handmade ram vest ¥17,600() Gray/Ivory [] 1 [D… [+583 chars]
3rd rowKathmandu, Nepal  UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witn… [+2468 chars]
4th rowAt least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem… [+1826 chars]
5th rowScores of foreign citizens were killed, taken hostage or listed as missing after the Hamas terror group launched a major assault on Israel on October 7. More than 1,400, mainly civilians, have been … [+4658 chars]
ValueCountFrequency (%)
the 63280
 
4.2%
chars 43564
 
2.9%
a 31633
 
2.1%
of 29917
 
2.0%
to 27057
 
1.8%
in 26673
 
1.8%
and 24577
 
1.6%
21178
 
1.4%
on 13640
 
0.9%
is 12348
 
0.8%
Other values (114484) 1220424
80.6%
2024-02-15T15:57:55.977946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1434016
15.2%
e 783484
 
8.3%
a 613970
 
6.5%
t 515522
 
5.5%
i 496113
 
5.3%
n 489247
 
5.2%
r 477098
 
5.1%
o 475593
 
5.0%
s 460403
 
4.9%
h 282298
 
3.0%
Other values (678) 3417581
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6556533
69.4%
Space Separator 1442188
 
15.3%
Uppercase Letter 569060
 
6.0%
Decimal Number 343819
 
3.6%
Other Punctuation 238347
 
2.5%
Control 64316
 
0.7%
Math Symbol 61806
 
0.7%
Open Punctuation 60480
 
0.6%
Close Punctuation 59698
 
0.6%
Dash Punctuation 33789
 
0.4%
Other values (12) 15289
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
48
 
3.1%
29
 
1.9%
27
 
1.8%
26
 
1.7%
25
 
1.6%
25
 
1.6%
24
 
1.6%
23
 
1.5%
23
 
1.5%
22
 
1.4%
Other values (406) 1264
82.3%
Lowercase Letter
ValueCountFrequency (%)
e 783484
11.9%
a 613970
 
9.4%
t 515522
 
7.9%
i 496113
 
7.6%
n 489247
 
7.5%
r 477098
 
7.3%
o 475593
 
7.3%
s 460403
 
7.0%
h 282298
 
4.3%
l 279998
 
4.3%
Other values (73) 1682807
25.7%
Uppercase Letter
ValueCountFrequency (%)
S 48486
 
8.5%
A 47874
 
8.4%
T 43658
 
7.7%
I 35825
 
6.3%
C 34704
 
6.1%
E 31870
 
5.6%
N 31294
 
5.5%
M 26942
 
4.7%
R 26626
 
4.7%
P 26485
 
4.7%
Other values (44) 215296
37.8%
Other Punctuation
ValueCountFrequency (%)
, 83181
34.9%
. 61821
25.9%
43746
18.4%
: 13069
 
5.5%
/ 9910
 
4.2%
' 9778
 
4.1%
" 5987
 
2.5%
% 2895
 
1.2%
? 1952
 
0.8%
; 1750
 
0.7%
Other values (23) 4258
 
1.8%
Control
ValueCountFrequency (%)
38070
59.2%
26197
40.7%
Œ 14
 
< 0.1%
ˆ 8
 
< 0.1%
€ 6
 
< 0.1%
† 5
 
< 0.1%
‡ 4
 
< 0.1%
… 3
 
< 0.1%
“ 2
 
< 0.1%
’ 2
 
< 0.1%
Other values (4) 5
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 44795
72.5%
< 7957
 
12.9%
> 7806
 
12.6%
| 828
 
1.3%
× 196
 
0.3%
= 120
 
0.2%
~ 75
 
0.1%
± 14
 
< 0.1%
10
 
< 0.1%
2
 
< 0.1%
Other values (3) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 70172
20.4%
1 50719
14.8%
0 49825
14.5%
3 38888
11.3%
4 25729
 
7.5%
5 25721
 
7.5%
6 21601
 
6.3%
9 20944
 
6.1%
7 20548
 
6.0%
8 19671
 
5.7%
Open Punctuation
ValueCountFrequency (%)
[ 45335
75.0%
( 15072
 
24.9%
{ 32
 
0.1%
20
 
< 0.1%
13
 
< 0.1%
5
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
© 393
65.0%
® 167
27.6%
° 36
 
6.0%
4
 
0.7%
2
 
0.3%
1
 
0.2%
1
 
0.2%
1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
] 45301
75.9%
) 14353
 
24.0%
} 26
 
< 0.1%
11
 
< 0.1%
4
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
Format
ValueCountFrequency (%)
43
48.9%
­ 23
26.1%
18
20.5%
 2
 
2.3%
1
 
1.1%
1
 
1.1%
Currency Symbol
ValueCountFrequency (%)
$ 3036
93.6%
£ 107
 
3.3%
¥ 64
 
2.0%
32
 
1.0%
¢ 6
 
0.2%
Modifier Symbol
ValueCountFrequency (%)
^ 163
86.2%
` 11
 
5.8%
¨ 5
 
2.6%
´ 5
 
2.6%
¯ 5
 
2.6%
Other Number
ValueCountFrequency (%)
² 25
53.2%
³ 16
34.0%
½ 4
 
8.5%
¾ 1
 
2.1%
¹ 1
 
2.1%
Space Separator
ValueCountFrequency (%)
1434016
99.4%
  8165
 
0.6%
6
 
< 0.1%
1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 31182
92.3%
2085
 
6.2%
520
 
1.5%
2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
4205
65.7%
1274
 
19.9%
» 915
 
14.3%
4
 
0.1%
Initial Punctuation
ValueCountFrequency (%)
1603
56.8%
« 995
35.3%
223
 
7.9%
Modifier Letter
ValueCountFrequency (%)
50
98.0%
1
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 304
100.0%
Letter Number
ValueCountFrequency (%)
3
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7125612
75.4%
Common 2318132
 
24.5%
Han 589
 
< 0.1%
Hiragana 469
 
< 0.1%
Katakana 355
 
< 0.1%
Arabic 90
 
< 0.1%
Inherited 63
 
< 0.1%
Cyrillic 12
 
< 0.1%
Greek 3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
14
 
2.4%
11
 
1.9%
11
 
1.9%
9
 
1.5%
9
 
1.5%
8
 
1.4%
8
 
1.4%
7
 
1.2%
7
 
1.2%
7
 
1.2%
Other values (268) 498
84.6%
Latin
ValueCountFrequency (%)
e 783484
 
11.0%
a 613970
 
8.6%
t 515522
 
7.2%
i 496113
 
7.0%
n 489247
 
6.9%
r 477098
 
6.7%
o 475593
 
6.7%
s 460403
 
6.5%
h 282298
 
4.0%
l 279998
 
3.9%
Other values (126) 2251886
31.6%
Common
ValueCountFrequency (%)
1434016
61.9%
, 83181
 
3.6%
2 70172
 
3.0%
. 61821
 
2.7%
1 50719
 
2.2%
0 49825
 
2.1%
[ 45335
 
2.0%
] 45301
 
2.0%
+ 44795
 
1.9%
43746
 
1.9%
Other values (121) 389221
 
16.8%
Katakana
ValueCountFrequency (%)
25
 
7.0%
23
 
6.5%
20
 
5.6%
19
 
5.4%
19
 
5.4%
16
 
4.5%
16
 
4.5%
16
 
4.5%
13
 
3.7%
12
 
3.4%
Other values (55) 176
49.6%
Hiragana
ValueCountFrequency (%)
48
 
10.2%
29
 
6.2%
27
 
5.8%
26
 
5.5%
25
 
5.3%
24
 
5.1%
23
 
4.9%
22
 
4.7%
22
 
4.7%
21
 
4.5%
Other values (38) 202
43.1%
Arabic
ValueCountFrequency (%)
ا 14
15.6%
ی 14
15.6%
ک 8
 
8.9%
ر 5
 
5.6%
ن 4
 
4.4%
م 4
 
4.4%
س 4
 
4.4%
و 4
 
4.4%
ں 3
 
3.3%
ت 3
 
3.3%
Other values (14) 27
30.0%
Inherited
ValueCountFrequency (%)
43
68.3%
18
28.6%
2
 
3.2%
Greek
ValueCountFrequency (%)
β 2
66.7%
α 1
33.3%
Cyrillic
ValueCountFrequency (%)
е 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9355125
99.0%
Punctuation 53770
 
0.6%
None 34796
 
0.4%
CJK 588
 
< 0.1%
Hiragana 469
 
< 0.1%
Katakana 413
 
< 0.1%
Arabic 90
 
< 0.1%
Currency Symbols 32
 
< 0.1%
Cyrillic 12
 
< 0.1%
Arrows 12
 
< 0.1%
Other values (8) 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1434016
15.3%
e 783484
 
8.4%
a 613970
 
6.6%
t 515522
 
5.5%
i 496113
 
5.3%
n 489247
 
5.2%
r 477098
 
5.1%
o 475593
 
5.1%
s 460403
 
4.9%
h 282298
 
3.0%
Other values (87) 3327381
35.6%
Punctuation
ValueCountFrequency (%)
43746
81.4%
4205
 
7.8%
2085
 
3.9%
1603
 
3.0%
1274
 
2.4%
520
 
1.0%
223
 
0.4%
43
 
0.1%
20
 
< 0.1%
18
 
< 0.1%
Other values (11) 33
 
0.1%
None
ValueCountFrequency (%)
  8165
23.5%
é 5060
14.5%
ó 2113
 
6.1%
á 2041
 
5.9%
ä 1871
 
5.4%
ü 1549
 
4.5%
í 1460
 
4.2%
ö 1060
 
3.0%
à 996
 
2.9%
« 995
 
2.9%
Other values (130) 9486
27.3%
Katakana
ValueCountFrequency (%)
50
 
12.1%
25
 
6.1%
23
 
5.6%
20
 
4.8%
19
 
4.6%
19
 
4.6%
16
 
3.9%
16
 
3.9%
16
 
3.9%
13
 
3.1%
Other values (57) 196
47.5%
Hiragana
ValueCountFrequency (%)
48
 
10.2%
29
 
6.2%
27
 
5.8%
26
 
5.5%
25
 
5.3%
24
 
5.1%
23
 
4.9%
22
 
4.7%
22
 
4.7%
21
 
4.5%
Other values (38) 202
43.1%
Currency Symbols
ValueCountFrequency (%)
32
100.0%
Arabic
ValueCountFrequency (%)
ا 14
15.6%
ی 14
15.6%
ک 8
 
8.9%
ر 5
 
5.6%
ن 4
 
4.4%
م 4
 
4.4%
س 4
 
4.4%
و 4
 
4.4%
ں 3
 
3.3%
ت 3
 
3.3%
Other values (14) 27
30.0%
CJK
ValueCountFrequency (%)
14
 
2.4%
11
 
1.9%
11
 
1.9%
9
 
1.5%
9
 
1.5%
8
 
1.4%
8
 
1.4%
7
 
1.2%
7
 
1.2%
7
 
1.2%
Other values (267) 497
84.5%
Cyrillic
ValueCountFrequency (%)
е 12
100.0%
Arrows
ValueCountFrequency (%)
10
83.3%
1
 
8.3%
1
 
8.3%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%
Number Forms
ValueCountFrequency (%)
3
100.0%
Math Operators
ValueCountFrequency (%)
2
100.0%
Misc Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
VS
ValueCountFrequency (%)
2
100.0%
Latin Ext Additional
ValueCountFrequency (%)
2
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
Distinct157
Distinct (%)0.3%
Missing30
Missing (%)0.1%
Memory size711.8 KiB
2024-02-15T15:57:56.500659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length20
Mean length7.319356
Min length3

Characters and Unicode

Total characters333221
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowNepal
2nd rowNepal
3rd rowNepal
4th rowNepal
5th rowNepal
ValueCountFrequency (%)
love 1460
 
2.9%
food 1457
 
2.9%
africa 1084
 
2.2%
home 999
 
2.0%
politics 986
 
2.0%
climate 961
 
1.9%
games 942
 
1.9%
space 864
 
1.7%
stock 841
 
1.7%
real 831
 
1.7%
Other values (162) 39660
79.2%
2024-02-15T15:57:57.327072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35042
 
10.5%
e 30230
 
9.1%
i 28489
 
8.5%
o 21469
 
6.4%
t 21377
 
6.4%
n 20226
 
6.1%
r 18990
 
5.7%
l 14632
 
4.4%
s 14013
 
4.2%
c 13636
 
4.1%
Other values (42) 115117
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 277452
83.3%
Uppercase Letter 51109
 
15.3%
Space Separator 4559
 
1.4%
Other Punctuation 101
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35042
12.6%
e 30230
10.9%
i 28489
10.3%
o 21469
 
7.7%
t 21377
 
7.7%
n 20226
 
7.3%
r 18990
 
6.8%
l 14632
 
5.3%
s 14013
 
5.1%
c 13636
 
4.9%
Other values (15) 59348
21.4%
Uppercase Letter
ValueCountFrequency (%)
A 6565
12.8%
S 5011
 
9.8%
P 4636
 
9.1%
C 3958
 
7.7%
I 3733
 
7.3%
F 2988
 
5.8%
H 2670
 
5.2%
M 2135
 
4.2%
N 2051
 
4.0%
G 1965
 
3.8%
Other values (15) 15397
30.1%
Space Separator
ValueCountFrequency (%)
4559
100.0%
Other Punctuation
ValueCountFrequency (%)
, 101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 328561
98.6%
Common 4660
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35042
 
10.7%
e 30230
 
9.2%
i 28489
 
8.7%
o 21469
 
6.5%
t 21377
 
6.5%
n 20226
 
6.2%
r 18990
 
5.8%
l 14632
 
4.5%
s 14013
 
4.3%
c 13636
 
4.2%
Other values (40) 110457
33.6%
Common
ValueCountFrequency (%)
4559
97.8%
, 101
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 333221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35042
 
10.5%
e 30230
 
9.1%
i 28489
 
8.5%
o 21469
 
6.4%
t 21377
 
6.4%
n 20226
 
6.1%
r 18990
 
5.7%
l 14632
 
4.4%
s 14013
 
4.2%
c 13636
 
4.1%
Other values (42) 115117
34.5%

full_content
Text

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Distinct (%)97.6%
Missing42986
Missing (%)94.4%
Memory size711.8 KiB
2024-02-15T15:57:58.019838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length177956
Median length5081.5
Mean length6733.6751
Min length1

Characters and Unicode

Total characters17305545
Distinct characters847
Distinct categories24 ?
Distinct scripts10 ?
Distinct blocks27 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2451 ?
Unique (%)95.4%

Sample

1st rowUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon. "The rooftops of the world are caving in," Guterres said on a visit to the Everest region in mountainous Nepal, adding that the country had lost nearly a third of its ice in just over three decades. "Glaciers are icy reservoirs -- the ones here in the Himalayas supply fresh water to well over a billion people," he said. "When they shrink, so do river flows." Nepal's glaciers melted 65 percent faster in the last decade than in the previous one, said Guterres, who is on a four-day visit to Nepal. Glaciers in the wider Himalayan and Hindu Kush ranges are a crucial water source for around 240 million people in the mountainous regions, as well as for another 1.65 billion people in the South Asian and Southeast Asian river valleys below. The glaciers feed 10 of the world's most important river systems, including the Ganges, Indus, Yellow, Mekong and Irrawaddy, and directly or indirectly supply billions of people with food, energy, clean air and income. Scientists say they are melting faster than ever before due to climate change, exposing communities to unpredictable and costly disasters. "I am here today to cry out from the rooftop of the world: stop the madness", Guterres said, speaking from Syangboche village, with the icy peak of the world's highest mountain Everest towering behind him. "The glaciers are retreating, but we cannot. We must end the fossil fuel age," he said. The world has warmed an average of nearly 1.2 degrees Celsius since the mid-1800s, unleashing a cascade of extreme weather, including more intense heatwaves, more severe droughts and storms made more ferocious by rising seas. Hardest hit are the most vulnerable people and the world's poorest countries, which have done little to contribute to the fossil fuel emissions that drive up temperatures. "We must act now to protect people on the frontline, and to limit global temperature rise to 1.5 degrees, to avert the worst of climate chaos," Guterres said. "The world can't wait." In the first phase of climate change's effects, melting glaciers can trigger destructive floods. "Melting glaciers mean swollen lakes and rivers flooding, sweeping away entire communities", he added. But all too soon, glaciers will dry up if change is not made, he warned. "In the future, major Himalayan rivers like the Indus, the Ganges and Brahmaputra could have massively reduced flows, he said. "That spells catastrophe".
2nd rowAt least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem to have been triggered by a combination of factors. A cloud burst ripped apart the South Shonak Lake — a glacial body in the state’s northwest. According to Sikkim’s Disaster Management Authority (SDMA), the Teesta has inundated at least four districts in the state. The calamity was aggravated by the release of water from the Chungthang dam — initial reports suggest that the breach was caused by water rushing from the mountains. Disaster management authorities are also investigating the possibility of the event being triggered by an earthquake in Nepal on Tuesday. The probe is likely to throw more light on the immediate causes of the flash flood but one thing has long been clear — states in the Himalayan region must respect the fragile ecology of the mountains and put in adequate safeguards to mitigate the damage caused by increasingly frequent extreme rainfall events. For years, studies have red-flagged the South Shonak Lake’s expansion due to glacial melting and warned that the water body is susceptible to breaches. In 2021, for instance, a study by scientists from IIT Roorkee, Indian Institute of ScienceBangalore, University of Dayton, USA, University of Graz, Austria, and the Universities of Zurich and Geneva in Switzerland recommended regular monitoring of the lake’s growth and continuous assessment of the region’s slope stability. The National Disaster Management Authority guidelines also say that risk reduction has to begin with mapping such water bodies, taking structural measures to prevent their breach and establishing mechanisms that can alert people about glacial lake outbursts. The IMD has collaborated with the US National Weather Service to warn people about six to 24 hours before a flash flood. But the system doesn’t seem to have come to terms with the Himalayan region’s idiosyncrasies. The Northeast has a key place in the hydel power push of successive governments at the Centre. The Chungthang Dam is a part of the 1,200 MW Teesta Stage 3 Hydroelectric Project. The government claims that such projects are climate-friendly because of their low emissions intensity. Hydroelectric power is also a major source of revenue for Sikkim. Ecologists, however, caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday’s disaster is a warning to take such caveats seriously and install robust safety mechanisms.
3rd rowIndia, the first non-Arab country to recognise the PLO in the 1970s, is now seen closer to Israel and its biggest benefactor, the United States. New Delhi, India– Israel’srelentless bombingof the besieged Gaza Strip and killing of nearly 6,000 people – a third of them children – in two weeks has outraged people across the world, triggering mass protests and a call for an immediate ceasefire. However, in India – the first non-Arab country to recognise the Palestine Liberation Organization (PLO), but now seen closer to Israel and its biggest benefactor, the United States – some pro-Palestine protesters reported being targeted by the government. Less than a week after the Gaza assault began, police in Hamirpur district of India’s most populous Uttar Pradesh state were looking for Muslim scholars Atif Chaudhary and Suhail Ansari. Their alleged crime: putting a WhatsApp display photo that said: “I stand with Palestine.” The two men were charged with promoting enmity between social groups. Ansari is under arrest, while Chaudhary is on the run, according to the police. In the same state, governed by the Hindu nationalist Bharatiya Janata Party (BJP), four students of the Aligarh Muslim University were booked by the police after they took out a pro-Palestine march on the campus a day after the Gaza assault began on October 7. However, when the Hindu far-right group Bajrang Dal took out a pro-Israel march in the same Aligarh city, raising slogans such as “Down with Palestine, Down with Hamas”, no action was taken against them by the authorities. In the national capital, New Delhi, there have been several examples of people being detained during rallies organised by student groups, activists and citizens for solidarity with the Palestinians since October 7. In the western state of Maharashtra, also governed by the BJP in alliance with a regional party, two protesters, Ruchir Lad and Supreeth Ravish, were arrested on October 13 for holding a march against the war on Gaza and charged with unlawful assembly. Pooja Chinchole, member of the Revolutionary Workers Party of India and one of the organisers of the protest held in state capital Mumbai, told Al Jazeera the police “created many hurdles before us when they got to know that we are organising a pro-Palestine protest”. “They detained one of the organisers a day before the protest and three organisers on the morning of the protest. When we still gathered to protest, they snatched our microphone, placards, and after a while, started using force on some of us,” she said. The crackdown, however, was not limited to the BJP-ruled states only. In the southern Karnataka state, governed by the main opposition Congress party, police charged 10 activists with creating a public nuisance after they organised a silent march in support of the Palestinians on October 16 in Bengaluru, the capital of the state. The Karnataka police also arrested a 58-year-old Muslim man for allegedly posting a video in support of Hamas on WhatsApp. Police also briefly detained Alam Nawaz, a Muslim government employee, for updating his WhatsApp status with a Palestinian flag and “Long Live Palestine” message. “People started seeing me with suspicion as if I have committed some crime by expressing my solidarity with Palestinian people,” Nawaz, 20, told Al Jazeera. All this despite the Congress expressing its support for the “rights of the Palestinian people to land, self-government and to live with dignity” as the party called for an immediate ceasefire in a resolution passed by its working committee on October 9. Meanwhile, pro-Israel rallies, organised mainly byHindu right-wing groups, were seen across India, while many on social media offered their services to the Israeli forces. On Saturday, dozens of supporters of a retired Indian army soldier travelled 182km (113 miles) to reach the Israeli embassy in New Delhi where they offered to go to Israel to fight against the Palestinians in Gaza. Last week, one of India’s most influential Hindu nationalists, Yati Narsinghanand, released a video in which he said Hindus and Jews “have the same enemy: Muhammad and his satanic book” as he urged the Israeli government to allow 1,000 Hindus to settle in Israel in order to “take on those Muslims”. Israel’s ambassador to India, Naor Gilon, on October 8 said he had received several requests from Indians wanting to voluntarily fight for Israel. Apoorvanand, professor of Hindi language at Delhi University, told Al Jazeera he was not surprised that the Hindu far right, which openly admires Adolf Hitler for his action against the Jews, is now supporting the Zionists in Israel. “Hindu far-right organisations in India have always supported those who dominate by violence. Hitler did once, so they supported him. Now Israel is doing this, so they are supporting it,” he said. Apoorvanand said the Hindu right in India thinks there are ideological linkages between them and the Zionists in Israel. “It looks like Israel is fighting a proxy war on behalf of the Hindu far right. They think Israel is fighting and decimating Muslims on their behalf. The way they want to establish Akhand Bharat [Unified India] by joining Pakistan, Afghanistan, Nepal together with India, they think Israel is following the same expansionist ideology,” he said. This was not always the case. India’s foreign policy has historically supported the Palestinian cause, which began with India voting against the United Nations resolution to create the state of Israel in 1947 and then recognising the PLO as a representative of the Palestinian people in 1974. India’s pro-Palestine stand was guided by the shared history of colonisation by the British, Zikrur Rahman, former Indian ambassador to Palestine, told Al Jazeera. “In the postcolonial era, we identified that this is a colonial attempt to divide the country and to create another country. We were not in favour of the creation of a country on the basis of religion,” he said. Rahman, however, added that while India’s position on Palestine has not changed, it is not as strong as it used to be. India recognised the creation of Israel in 1950, but did not establish diplomatic relations until 1992, when the details of the firstOslo Accordwere being finalised. Since then, India has tried to strike a balance between its strategic relations with Israel and sympathising with the Palestinian struggle. Today, India is the largest buyer of Israeli-made weapons, while strategic and security cooperation between them has grown manifold. Comparisons have also been made between Israel demolishing homes of Palestinians in the occupied territories and a similar policy adopted by some BJP state governmentsmainly against Muslimsas forms of “collective punishment” of the community. Since Prime Minister Narendra Modi came to power in 2014, he has made public statements, calling his Israeli counterpart Benjamin Netanyahu a “good friend” on several occasions. Modi was one of the first global leaders to post his solidarity with Israel after Hamas’s unprecedented incursion on October 7. “Deeply shocked by the news of terrorist attacks in Israel,” said his post on X, which came four hours before US President Joe Biden reacted to the event. Modi also condemned the Israeli attack onal-Ahli Arab Hospitalin Gaza on October 18, in which nearly 500 Palestinians were killed, though his message on X appeared nearly eight hours after Biden’s post. Meanwhile, India’s Ministry of External Affairs issued a statement on October 12, reiterating New Delhi’s position of establishing a “sovereign, independent, and viable state of Palestine, living within secure and recognised borders, side by side at peace with Israel”. Last week, Modi posted on X about his phone call with the Palestinian Authority President Mahmoud Abbas, in which he repeated India’s “longstanding principled position on the Israel-Palestine issue”. He said his government is sending humanitarian assistance for the besieged residents of Gaza. Journalist Anand K Sahay, however, thinks India’s response to the unfolding humanitarian disaster in Gaza has not been adequate. “What India didn’t say is important. India didn’t demand a ceasefire. Historically, India has always demanded a ceasefire in case of a [foreign] war. In this case also we should have strongly said: stop the war,” he told Al Jazeera. Sahay said Modi’s flaunting of closeness with Israel is also aimed at appeasing his core vote bank: the Hindus. “Suppose there was another religion in majority in Palestine. Then our stand may have been different. During the Russia-Ukraine war, we said ‘this is not an age of war’. Why couldn’t we say this in case of Israel-Palestine war?” asked Sahay. “By not asking for a ceasefire, India was also indirectly signalling the US that the Indian position was very close to the US line.” Follow Al Jazeera English:
4th rowWritten by Alex Travelli and Hari Kumar No nation in the world is buying as many airplanes as India. Its largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparalleled in aviation. In NewDelhi, Indira Gandhi International Airport will be ready for 109 million passengers next year, as it prepares to become the world’s second busiest, behind Hartsfield-Jackson Atlanta International Airport in the United States. And this is happening in a vast country still heavily reliant on trains — with 20 journeys by rail for every one by air. The enormous aviation build-out, with a surge of investment behind it, has pride of place in India’s case for a greater standing on the world stage. As it moves up the ranks of the world’s biggest economies, India is scrambling to meet the expanding ambitions of its ascendant middle class. Its airports present highly visible achievements. Air travel remains out of the financial reach of most Indians. An estimated 3% of the country’s population flies on a regular basis. But in a nation of 1.4 billion people, that percentage represents 42 million — executives, students and engineers who yearn to get quickly from here to there inside India’s borders, and to gain easier access to destinations beyond, for both business and vacation. Kapil Kaul, CEO of CAPA India, an advisory firm focused on aviation, calls “the next two to three years critical for achieving the quality of growth that India desires and deserves.” Growth has so far been profitless. Now Indian aviation must prove it can make money. The effects of the spending spree should redound across India’s economy. Cargo comes with passenger traffic, and foreign investment tends to follow closely behind, Kaul said. Arrivals to the international terminal at Indira Gandhi Airport are greeted by a wall of giant sculptural hands, their fingers and palms folded into the signifying shapes of the Buddha’s gestures, looking both ancient and futuristic. In 2012, when they were installed, 30 million passengers passed through the airport. By the time the airport has expanded to its new capacity, another one will have been built from scratch on the other side of the city. Indira Gandhi Airport is racing to get bigger. In July it added a fourth runway and opened an elevated taxiway. The company that operates it, GMR Airports, took over in 2006, a time when all arrivals walked past cows lazing in the dust to reach a taxi stand. By 2018 the facility was rated as India’s most valuable infrastructural asset. To spare the use of jet fuel, a battery-powered TaxiBot lugs idling planes around the tarmac. An automated luggage-handling system can sort 6,000 bags an hour. Two beneficiaries of India’s expanding aviation market are the world’s largest airplane makers: Boeing in America and Airbus in Europe. In February, Air India, which Tata Group took private last year, agreed to buy 250 planes from Airbus and 220 from Boeing, worth a combined $70 billion. In June, IndiGo, the country’s biggest carrier by passengers and flights, ordered 500 new Airbus A230s. The bulk of the growth of Indian aviation has been among homegrown airlines, which have clocked a 36% increase in passengers since 2022. Foreign tourist arrivals are rebounding since the pandemic, but are still relatively scarce, barely topping 10 million in a good year (about the same as Romania). So low-cost carriers are adding new countries to their destinations in order to accommodate India’s demand for foreign tourism. Azerbaijan, Kenya and Vietnam are all a direct flight from Delhi orMumbai, India’s financial capital, for less than 21,000 rupees ($250) one way. The air corridor between Delhi and Mumbai was already one of the world’s 10 busiest. Like Delhi, Mumbai has new airport terminals that would be the envy of any city in America, not to mention the glorious new all-bamboo Terminal 2 at Kempegowda International Airport in Bengaluru, a city in southern India. But the expansion in infrastructure is not limited to the country’s premier metropolitan areas. Prime MinisterNarendra Modi’s government likes to point out that the number of airports has doubled in the nine years since he took office, to 148 from 74. Jyotiraditya Scindia, Modi’s aviation minister, said there would be at least 230 by 2030. The government has invested more than $11 billion in airports over the past decade, and Scindia has promised another $15 billion. That means that sleepy towns such asDarbhanga, a former principality in the impoverished state of Bihar in eastern India, now have nonstop access to Delhi, Bengaluru and beyond. For many of the 900 travelers a day who fill its flights, including plenty from nearby Nepal, the new airport has transformed the journey. Prasanna Kumar Jha, 52, was born in Darbhanga but works in Delhi as a tax consultant. “Who ever expected that Darbhanga would be on the air map?” he asked. Flying to his hometown on short notice to see his ailing mother cost him 10,500 rupees ($126), which pinched. “But if you calculate the alternative — by train from Delhi and then taxi to Darbhanga — it will take at least 30 hours,” he said. “The plane journey is no longer a luxury but a necessity.” Darbhanga’s airport is a far cry from New Delhi’s. There is no parking lot. Passengers walk from the edge of a highway past a checkpoint to wait on benches outside the terminal. Then they wait on another set of outdoor benches after clearing the security check. But it works. Another passenger on the same flight at Darbhanga, Ajay Jha, was cradling his 1-year-old daughter, Saranya, as he stood near the rudimentary baggage claim. His family was on the last leg of a trip that started in Bellevue, Washington, where he works as an engineer for Amazon, to a family reunion in the Bihari countryside. Traveling halfway around the world took less time than Jha used to spend getting home from his school in Bengaluru. Yet a vast majority of Indians cannot afford such conveniences. The annual mean income is still less than a single economy-class fare from the United States, and, in this top-heavy economy, most Indians earn much less than that. Middle class, in Indian parlance, indicates somewhere close to the top of the pyramid. A report by CAPA India counted just 0.13 passenger seats per capita in 2019 for Indians, compared with 0.52 for Chinese and 3.03 for Americans. But aviation companies and India’s elected officials look at the low penetration and see opportunity. A scarcity of competition, in the face of an emerging duopoly between IndiGo and the Tata-led airlines, is one of the new landscape’s most striking features. Smaller competitors keep going bust, most recently Go First, which declared bankruptcy in May. A shortage of pilots, after dozens were poached by bigger companies, forced Akasa Air, a promising upstart, to cancel flights in August. But supply shortages are not the worst kind of problem to have in today’s global economy. With aviation’s growth in the decade before the pandemic steady at about 15% a year, the Indian boom seems all but guaranteed to change the future of aviation worldwide. If the benefits accruing to the winners in India’s economy can be coaxed into trickling outward and downward, the same could go for many other sectors.
5th rowNEW DELHI: India preferred Bangladesh over Nepal for the post of regional director of World Health Organisation’s (WHO) Southeast Asia Region contributing to the comfortable victory of Saima Wazed, daughter of Bangla PM Sheikh Hasina .Although officials were tight-lipped about which way India voted, sources said that it voted for Wazed at the expense of Dr Shambhu Acharya, who could manage one vote, apart from Nepal’s.It could not have been an easy decision as India has been working to improve ties with both neighbours. The decision may have been influenced by Bangladesh PM Sheikh Hasina’s effort to stop Islamic terrorist groups, many of them backed by Pakistan, from using Bangladesh as a base to launch attacks against India. The personal stake of Sheikh Hasina, who faces an increasingly tough situation on the domestic front ahead of elections next year, in the outcome of a contest featuring her daughter and China’s effort to woo her, also perhaps helped tip scales in Wazed’s favour.Wazed, who is a global autism advocate and has been a member of the WHO’s 25-member expert advisory panel on mental health, would have won in any case as eight of the 10 member nations preferred her over Dr Acharya who is a WHO specialist.WHO said that Wazed’s nomination will be submitted to the WHO executive board during its 154th session, taking place on 22-27 January 2024 in Geneva, Switzerland, and that the newly appointed regional director will take office on 1 February 2024.The Bangladesh High Commission, in its statement announcing the victory, said their ministry of foreign affairs, in coordination with the ministry of health and family welfare, conducted an effective campaign in Wazed’s favour since the very beginning of the election process.
ValueCountFrequency (%)
the 134906
 
5.1%
and 78702
 
3.0%
of 77640
 
3.0%
to 64715
 
2.5%
in 54705
 
2.1%
a 47298
 
1.8%
for 26778
 
1.0%
on 23866
 
0.9%
23170
 
0.9%
is 22114
 
0.8%
Other values (112282) 2075791
78.9%
2024-02-15T15:57:59.115640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2697010
15.6%
e 1573260
 
9.1%
t 1132630
 
6.5%
a 1088891
 
6.3%
n 993497
 
5.7%
i 992605
 
5.7%
o 967681
 
5.6%
r 886985
 
5.1%
s 873179
 
5.0%
l 516278
 
3.0%
Other values (837) 5583529
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12766346
73.8%
Space Separator 2723286
 
15.7%
Uppercase Letter 671799
 
3.9%
Decimal Number 473815
 
2.7%
Other Punctuation 445115
 
2.6%
Dash Punctuation 58446
 
0.3%
Control 41412
 
0.2%
Close Punctuation 28330
 
0.2%
Open Punctuation 28194
 
0.2%
Final Punctuation 25109
 
0.1%
Other values (14) 43693
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
131
 
4.6%
82
 
2.9%
81
 
2.9%
61
 
2.2%
47
 
1.7%
39
 
1.4%
39
 
1.4%
38
 
1.3%
37
 
1.3%
37
 
1.3%
Other values (494) 2230
79.0%
Lowercase Letter
ValueCountFrequency (%)
e 1573260
12.3%
t 1132630
 
8.9%
a 1088891
 
8.5%
n 993497
 
7.8%
i 992605
 
7.8%
o 967681
 
7.6%
r 886985
 
6.9%
s 873179
 
6.8%
l 516278
 
4.0%
d 494661
 
3.9%
Other values (69) 3246679
25.4%
Other Symbol
ValueCountFrequency (%)
© 463
27.6%
® 429
25.6%
311
18.5%
° 168
 
10.0%
124
 
7.4%
25
 
1.5%
19
 
1.1%
10
 
0.6%
🇵 9
 
0.5%
7
 
0.4%
Other values (49) 112
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
A 62769
 
9.3%
T 59000
 
8.8%
S 58241
 
8.7%
C 50160
 
7.5%
I 49595
 
7.4%
P 37232
 
5.5%
E 35213
 
5.2%
M 31281
 
4.7%
N 28874
 
4.3%
R 27432
 
4.1%
Other values (36) 232002
34.5%
Other Punctuation
ValueCountFrequency (%)
, 179250
40.3%
. 175449
39.4%
" 19045
 
4.3%
' 16072
 
3.6%
: 15384
 
3.5%
% 14729
 
3.3%
/ 11449
 
2.6%
; 4913
 
1.1%
& 3402
 
0.8%
? 2290
 
0.5%
Other values (25) 3132
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 1274
68.0%
| 273
 
14.6%
= 116
 
6.2%
~ 89
 
4.7%
39
 
2.1%
> 34
 
1.8%
11
 
0.6%
< 10
 
0.5%
6
 
0.3%
5
 
0.3%
Other values (9) 17
 
0.9%
Decimal Number
ValueCountFrequency (%)
2 89887
19.0%
0 88529
18.7%
1 66284
14.0%
3 53690
11.3%
4 33593
 
7.1%
5 33495
 
7.1%
9 27557
 
5.8%
6 27482
 
5.8%
7 26848
 
5.7%
8 26434
 
5.6%
Other values (7) 16
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 27648
97.6%
] 612
 
2.2%
49
 
0.2%
10
 
< 0.1%
3
 
< 0.1%
} 3
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 27517
97.6%
[ 612
 
2.2%
48
 
0.2%
6
 
< 0.1%
3
 
< 0.1%
{ 3
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%
Private Use
ValueCountFrequency (%)
42
41.6%
20
19.8%
18
17.8%
9
 
8.9%
6
 
5.9%
4
 
4.0%
1
 
1.0%
1
 
1.0%
Other Number
ValueCountFrequency (%)
10
25.6%
² 9
23.1%
¹ 7
17.9%
½ 5
12.8%
3
 
7.7%
2
 
5.1%
2
 
5.1%
1
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 50599
86.6%
5637
 
9.6%
2131
 
3.6%
34
 
0.1%
22
 
< 0.1%
12
 
< 0.1%
11
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 24304
97.0%
423
 
1.7%
¥ 165
 
0.7%
£ 146
 
0.6%
21
 
0.1%
¢ 8
 
< 0.1%
1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
5
22.7%
5
22.7%
4
18.2%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
Space Separator
ValueCountFrequency (%)
2697010
99.0%
  26172
 
1.0%
57
 
< 0.1%
24
 
< 0.1%
15
 
< 0.1%
8
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
^ 15
50.0%
´ 4
 
13.3%
🏽 4
 
13.3%
🏻 4
 
13.3%
🏼 2
 
6.7%
` 1
 
3.3%
Final Punctuation
ValueCountFrequency (%)
15456
61.6%
9496
37.8%
» 138
 
0.5%
19
 
0.1%
Modifier Letter
ValueCountFrequency (%)
54
94.7%
ˈ 1
 
1.8%
1
 
1.8%
ː 1
 
1.8%
Nonspacing Mark
ValueCountFrequency (%)
8
61.5%
̈ 2
 
15.4%
̱ 2
 
15.4%
́ 1
 
7.7%
Control
ValueCountFrequency (%)
37081
89.5%
4330
 
10.5%
ƒ 1
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
9825
95.8%
426
 
4.2%
« 9
 
0.1%
Format
ValueCountFrequency (%)
30
71.4%
­ 6
 
14.3%
6
 
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1687
100.0%
Enclosing Mark
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13438143
77.7%
Common 3864441
 
22.3%
Arabic 1110
 
< 0.1%
Hiragana 593
 
< 0.1%
Han 590
 
< 0.1%
Katakana 498
 
< 0.1%
Unknown 101
 
< 0.1%
Hangul 30
 
< 0.1%
Greek 25
 
< 0.1%
Inherited 14
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
11
 
1.9%
10
 
1.7%
10
 
1.7%
10
 
1.7%
10
 
1.7%
9
 
1.5%
9
 
1.5%
9
 
1.5%
9
 
1.5%
8
 
1.4%
Other values (258) 495
83.9%
Common
ValueCountFrequency (%)
2697010
69.8%
, 179250
 
4.6%
. 175449
 
4.5%
2 89887
 
2.3%
0 88529
 
2.3%
1 66284
 
1.7%
3 53690
 
1.4%
- 50599
 
1.3%
37081
 
1.0%
4 33593
 
0.9%
Other values (188) 393069
 
10.2%
Latin
ValueCountFrequency (%)
e 1573260
11.7%
t 1132630
 
8.4%
a 1088891
 
8.1%
n 993497
 
7.4%
i 992605
 
7.4%
o 967681
 
7.2%
r 886985
 
6.6%
s 873179
 
6.5%
l 516278
 
3.8%
d 494661
 
3.7%
Other values (114) 3918476
29.2%
Arabic
ValueCountFrequency (%)
131
 
11.8%
82
 
7.4%
61
 
5.5%
39
 
3.5%
39
 
3.5%
38
 
3.4%
37
 
3.3%
32
 
2.9%
31
 
2.8%
28
 
2.5%
Other values (91) 592
53.3%
Katakana
ValueCountFrequency (%)
37
 
7.4%
34
 
6.8%
24
 
4.8%
24
 
4.8%
23
 
4.6%
21
 
4.2%
17
 
3.4%
17
 
3.4%
16
 
3.2%
16
 
3.2%
Other values (52) 269
54.0%
Hiragana
ValueCountFrequency (%)
81
 
13.7%
47
 
7.9%
35
 
5.9%
33
 
5.6%
30
 
5.1%
29
 
4.9%
23
 
3.9%
21
 
3.5%
21
 
3.5%
20
 
3.4%
Other values (36) 253
42.7%
Hangul
ValueCountFrequency (%)
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (17) 17
56.7%
Unknown
ValueCountFrequency (%)
42
41.6%
20
19.8%
18
17.8%
9
 
8.9%
6
 
5.9%
4
 
4.0%
1
 
1.0%
1
 
1.0%
Greek
ValueCountFrequency (%)
β 7
28.0%
σ 5
20.0%
α 4
16.0%
Ε 2
 
8.0%
Β 2
 
8.0%
Ι 2
 
8.0%
Τ 2
 
8.0%
Δ 1
 
4.0%
Inherited
ValueCountFrequency (%)
8
57.1%
̈ 2
 
14.3%
̱ 2
 
14.3%
1
 
7.1%
́ 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17228055
99.6%
Punctuation 43639
 
0.3%
None 30792
 
0.2%
Katakana 619
 
< 0.1%
Hiragana 593
 
< 0.1%
CJK 589
 
< 0.1%
Currency Symbols 444
 
< 0.1%
Geometric Shapes 369
 
< 0.1%
Letterlike Symbols 125
 
< 0.1%
PUA 101
 
< 0.1%
Other values (17) 219
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2697010
15.7%
e 1573260
 
9.1%
t 1132630
 
6.6%
a 1088891
 
6.3%
n 993497
 
5.8%
i 992605
 
5.8%
o 967681
 
5.6%
r 886985
 
5.1%
s 873179
 
5.1%
l 516278
 
3.0%
Other values (87) 5506039
32.0%
None
ValueCountFrequency (%)
  26172
85.0%
© 463
 
1.5%
® 429
 
1.4%
á 277
 
0.9%
é 226
 
0.7%
° 168
 
0.5%
¥ 165
 
0.5%
£ 146
 
0.5%
» 138
 
0.4%
131
 
0.4%
Other values (232) 2477
 
8.0%
Punctuation
ValueCountFrequency (%)
15456
35.4%
9825
22.5%
9496
21.8%
5637
 
12.9%
2131
 
4.9%
426
 
1.0%
253
 
0.6%
184
 
0.4%
57
 
0.1%
30
 
0.1%
Other values (12) 144
 
0.3%
Currency Symbols
ValueCountFrequency (%)
423
95.3%
21
 
4.7%
Geometric Shapes
ValueCountFrequency (%)
311
84.3%
25
 
6.8%
19
 
5.1%
7
 
1.9%
6
 
1.6%
1
 
0.3%
Letterlike Symbols
ValueCountFrequency (%)
124
99.2%
1
 
0.8%
Hiragana
ValueCountFrequency (%)
81
 
13.7%
47
 
7.9%
35
 
5.9%
33
 
5.6%
30
 
5.1%
29
 
4.9%
23
 
3.9%
21
 
3.5%
21
 
3.5%
20
 
3.4%
Other values (36) 253
42.7%
Katakana
ValueCountFrequency (%)
67
 
10.8%
54
 
8.7%
37
 
6.0%
34
 
5.5%
24
 
3.9%
24
 
3.9%
23
 
3.7%
21
 
3.4%
17
 
2.7%
17
 
2.7%
Other values (54) 301
48.6%
PUA
ValueCountFrequency (%)
42
41.6%
20
19.8%
18
17.8%
9
 
8.9%
6
 
5.9%
4
 
4.0%
1
 
1.0%
1
 
1.0%
Alphabetic PF
ValueCountFrequency (%)
12
92.3%
1
 
7.7%
Math Operators
ValueCountFrequency (%)
11
52.4%
5
23.8%
3
 
14.3%
1
 
4.8%
1
 
4.8%
CJK
ValueCountFrequency (%)
11
 
1.9%
10
 
1.7%
10
 
1.7%
10
 
1.7%
10
 
1.7%
9
 
1.5%
9
 
1.5%
9
 
1.5%
9
 
1.5%
8
 
1.4%
Other values (257) 494
83.9%
Enclosed Alphanum
ValueCountFrequency (%)
10
55.6%
3
 
16.7%
2
 
11.1%
2
 
11.1%
1
 
5.6%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇵 9
15.5%
🇦 6
10.3%
🇭 5
8.6%
🇳 5
8.6%
🇬 4
 
6.9%
🇮 4
 
6.9%
🇯 4
 
6.9%
🇧 4
 
6.9%
🇿 3
 
5.2%
🇨 3
 
5.2%
Other values (7) 11
19.0%
VS
ValueCountFrequency (%)
8
100.0%
Dingbats
ValueCountFrequency (%)
6
60.0%
3
30.0%
1
 
10.0%
Number Forms
ValueCountFrequency (%)
5
22.7%
5
22.7%
4
18.2%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
Emoticons
ValueCountFrequency (%)
😡 4
40.0%
🙌 4
40.0%
😃 1
 
10.0%
😁 1
 
10.0%
Arrows
ValueCountFrequency (%)
3
100.0%
Latin Ext Additional
ValueCountFrequency (%)
2
28.6%
2
28.6%
2
28.6%
1
14.3%
Hangul
ValueCountFrequency (%)
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (17) 17
56.7%
Arabic
ValueCountFrequency (%)
م 2
22.2%
، 2
22.2%
ر 1
11.1%
ح 1
11.1%
ا 1
11.1%
د 1
11.1%
ی 1
11.1%
Diacriticals
ValueCountFrequency (%)
̈ 2
40.0%
̱ 2
40.0%
́ 1
20.0%
Alchemical
ValueCountFrequency (%)
🜃 1
100.0%
Modifier Letters
ValueCountFrequency (%)
ˈ 1
50.0%
ː 1
50.0%
Math Alphanum
ValueCountFrequency (%)
𝕏 1
100.0%
IPA Ext
ValueCountFrequency (%)
ɔ 1
100.0%

article
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Memory size711.8 KiB
2024-02-15T15:57:59.909264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length204800
Median length5066
Mean length7612.7678
Min length1

Characters and Unicode

Total characters22625146
Distinct characters894
Distinct categories24 ?
Distinct scripts13 ?
Distinct blocks30 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2972 ?
Unique (%)100.0%

Sample

1st rowpavyllon london, at four seasons hotel london at park lane one of the world’s best chefs has created a new culinary stage in london. yannick alléno, the superstar french chef who has nabbed an impressive 15 michelin stars, recently made his u.k. debut with the opening of pavyllon london. located in four seasons hotel london at park lane in fashionable mayfair, the new restaurant showcases alléno’s modern french cuisine with bespoke british influences. the fine dining destination offers guests a chance to experience alléno’s signature culinary trademarks — namely, modern french fare grounded in classical techniques, an innovative approach to sauce-making, and the belief that chefs have a responsibility to care for the good health of their guests — at his new refined-yet-relaxed culinary theater. "pavyllon london is an embodiment of my lifelong dream to redefine french dining," says alléno. "it combines my family's bistro-running legacy with my passion for culinary innovation. at pavyllon london, we look forward to welcoming our guests into the heart of the action, creating interactive, less formal dining, centered around countertop seating and simply great food.” chef yannick alléno chef yannick alléno pavyllon london is the latest restaurant from the prolific french-born yannick alléno, the superstar chef who’s been awarded 15 stars across 17 restaurants in paris, courchevel, monaco, dubai, and seoul. that puts alléno on a very short, elite list of michelin’s most-awarded chefs comprised of himself, joël robuchon, alain ducasse, gordon ramsay, pierre gagnaire, and martín berasategui. in fact, chef alléno’s trio of restaurants in pavillon ledoyen hold six stars (three at alléno paris, two at l’abysse and one at pavyllon), making it the most star-rated independent restaurant destination in the world. the lounge at pavyllon london at pavyllon london, which opened in july, chef yannick delivers his signature refined french fare with british influences like local and regional produce. the food, wine, and cocktail menus also feature specialty ingredients from time-honored purveyors like cheese from neal’s yard dairy and smoked salmon from h. forman & son in london, and sparkling wines from gusbourne vineyards in kent. alléno’s famed sauce-making skills and botanical extractions are on display throughout the menus. his groundbreaking methods utilize vacuum-cooking at precise temperatures, followed by cryo-concentration, to procure the purest flavor from produce and other ingredients. the result is vibrantly colored and flavored sauces that yield big flavor while reducing the amount of fat, sugar, and salt traditionally used. pavyllon london's menus showcase refined french cuisine with british influences pavyllon london’s cuisine pavyllon london serves breakfast, lunch, dinner, and high tea daily. crispy prawns with trout roe and miso beurre blanc a three-course lunch menu at pavyllon london might include crispy prawns set atop miso beurre blanc and trout roe, red mullet and clams with chermoula and chorizo butter, and a perfect baked peach baked in arlette crust with housemade red berry sorbet, topped with crunchy crystallized mint. the fine dining lunch set is designed to be eaten in just 55 minutes, meaning it’s possible to work in a decadent mid-day meal into an otherwise-ordinary work day. a refined version of surf and turf at dinner, guests can opt for à la carte options and kick off their meal with a duo of seabass carpaccio and beef tartare with puffed quinoa and crispy shallot to start, or a comté souffle with watercress coulis and eel butter. mains include a langoustine tart with caviar and beurre blanc, and veal cordon bleu with béchamel. desserts are inventive and intriguing, like roasted fig and curry ice cream with fig leaf cream, cardamom pastry crisp, and flambéed figs with marsala. highlights of the dinner tasting menus include warm potatoes glazed with an electric-green lovage mayonnaise, a reinvented prawn cocktail with fennel, dill, trout roe, and parmesan, lamb chops with shiso coulis and anchoïade, and beef filet with confit pears and flambéed cognac. high tea at pavyllon london at high tea, tiers of traditional fingers sandwiches are served alongside decadent chocolates and jewel-like miniature fruit tarts, meringues, and pastries covered in shiny mirror glaze. a sampling of small bites at bar antoine bar antoine before or after a meal at pavyllon, guests can stop off at bar antoine, helmed by head mixologist michele lombardi. the drinks menu ranges from classic cocktails to spirit-free libations, plus chef yannick’s signature creative gin & tonics made with seasonal fruit, vegetable, and herb extractions in a rainbow of vibrant colors. michele lombardi, head mixologist at bar antoine, crafts g&ts with botanical extractions bar antoine also offers shareable small plates, live jazz nights, and a lovely outdoor terrace for warmer months. a palette of soft blues and grays create a serene dining room design a sophisticated and serene palette of pearlescent whites, cool blues, and soft grays was curated by interior designer chahan minassian, the former european creative director of ralph lauren. the inspiration? “the chic conviviality of a parisian residence with the familiarity of a british club.” the tasteful lounge and main dining room feel sumptuous and serene, with gleaming marble, plush banquets, velvet sofas and cushions, contemporary art, and potted greenery like fiddle leaf figs. the chef's counter offers diners a front row seat to action in the kitchen the serene spaces are the ideal backdrop to showcase the culinary action of the open kitchen, which chef alléno views as a performance space. four seasons hotel london at park lane special holiday meals pavyllon london will be a destination for elevated festive holiday fare this winter, with special brunch, lunch, and dinner menus planned for christmas and new year’s. to learn more about pavyllon london in four seasons hotel london at park lane, or to book a reservation, visit pavyllon london.
2nd rownice moved into provisional first place in the ligue 1 standings thanks to a second-half hicham boudaoui goal that secured a 1-0 win at lowly clermont foot on friday. the result lifted nice to first place with 22 points, two points ahead of as monaco, who play at lille on sunday. clermont are second-bottom with five points. boudaoui struck in the 74th minute after running unmarked into the box and tapping home a well-placed cross. nice had a chance to open the scoring just before halftime when alidu seidu fouled boudaoui in the area but gaetan labord's penalty went wide, leaving the first half scoreless. francesco farioli's nice have had an exceptional start to the season, maintaining an unbeaten record and securing seven clean sheets, the best in europe's top five leagues.
3rd rowthe world’s frogs, salamanders, newts and other amphibians remain in serious trouble. a new global assessment has found that 41% of amphibian species that scientists have studied are threatened withextinction, meaning they are either vulnerable, endangered or critically endangered. that’s up from 39% reported in the last assessment, in 2004. “amphibians are the world's most threatened animals,” said duke university's junjie yao, a frog researcher who was not involved in the study. “their unique biology and permeable skin make them very sensitive to environmental changes." the study,published wednesdayin the journalnature, found that the loss of habitat from the expansion of farming and ranching is the single biggest threat to amphibians worldwide. but a growing percentage of amphibian species are now also pushed to the brink by novel diseases and climate change, the study found. read more:healthy biodiversity is the reason to fight climate change amphibians are especially vulnerable animals. they have distinct life stages that each often require separate habitats, so they can be disrupted by changes in either aquatic or land environments, said university of texas biologist michael ryan, who was not involved in the study. they are also at risk because of their delicate skin. most amphibians absorb oxygen to breathe through their skin, and so they do not have scales, feathers or fur to protect them. chemical pollution, bacteria and fungal infections impact them quickly, as do heightened swings in temperature and moisture levels due toclimate change. for example, frogs are usually nocturnal. if it’s too hot, they won’t come out even at night because they would lose too much water through their skin, said patricia burrowes, a study co-author and researcher at the national museum of natural sciences in madrid. but remaining in sheltered resting places limits frogs’ ability to eat and to breed. this summer was the hottest on record for the northern hemisphere, and 2023 is on track to be the 2nd hottest globally, after 2016. juan manuel guayasamin, a frog biologist at the university san francisco of quito, ecuador, said that advances in technology to trackanimalsand climate variations allowed the new study to use much more precise data than the 2004 assessment. “we have a much better understanding of some risks,” said guayasamin, who was not involved in the report. the study identified the greatest concentrations of threatened amphibian species in several biodiversity hotspots, including the caribbean islands, the tropical andes, madagascar and sri lanka. other locations with large numbers of threatened amphibians include brazil’s atlantic forest, southern china and the southeastern united states.
4th rowiron-rich sediment colors the red-orange waters of the betsiboka river delta in madagascar in this image taken by an astronaut on the international space station on sept. 30, 2023. the sediment can clog waterways in the delta's estuarial environment, but it can also form new islands that become colonized by mangroves.despite its rusty color, this artery of water is important for biodiversity. within the betsiboka river delta, the estuary supplies food, such as seagrasses, to the endangered green turtle and vulnerable dugong, or sea cow.provided bynasacitation: image: rusty red waters in madagascar (2023, october 31) retrieved 3 november 2023 from https://phys.org/news/2023-10-image-rusty-red-madagascar.htmlthis document is subject to copyright. apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. the content is provided for information purposes only. despite its rusty color, this artery of water is important for biodiversity. within the betsiboka river delta, the estuary supplies food, such as seagrasses, to the endangered green turtle and vulnerable dugong, or sea cow.provided bynasacitation: image: rusty red waters in madagascar (2023, october 31) retrieved 3 november 2023 from https://phys.org/news/2023-10-image-rusty-red-madagascar.htmlthis document is subject to copyright. apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. the content is provided for information purposes only. despite its rusty color, this artery of water is important for biodiversity. within the betsiboka river delta, the estuary supplies food, such as seagrasses, to the endangered green turtle and vulnerable dugong, or sea cow.provided bynasacitation: image: rusty red waters in madagascar (2023, october 31) retrieved 3 november 2023 from https://phys.org/news/2023-10-image-rusty-red-madagascar.htmlthis document is subject to copyright. apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. the content is provided for information purposes only. provided bynasa
5th roweverything ends. no, i’m not having an existential crisis; i’m looking atmax‘s lineup in november! while it’s true that the streamer will be adding lots of new shows and movies, it will also be saying goodbye to quite a few films and series in november. probably the most significant things leaving max this month are the amc+ showsadiscovery of witches,dark winds,killing eve,interview with the vampire,ride with norman reedus,fear the walking dead, andgangs of london. this isn’t surprising as they were on a three-month loan from the rival streamer. other movies leaving the service include theunderrated horror sequela nightmare on elm street 4, the steamy 1994 thrillerdisclosure, and the classic suburban nightmare filmpoltergeist. check out the list below and try to watch some of these gems before they leave max for good (or at least a long while). editor’s note: this list may not be comprehensive and is subject to change by the network/streamer. november 1 dark winds (amc+) a discovery of witches, season 1-3 (amc+) fear the walking dead, seasons 1-7 (amc+) gangs of london, seasons 1-2 (amc+) interview with the vampire (amc+) killing eve, seasons 1-4 (amc+) ride with norman reedus, seasons 1-5 (amc+) november 2 300: rise of an empire (2014) november 5 hard knocks: in season: the arizona cardinals (hbo original) november 6 the host (2013) november 7 noblesse we are not done yet (2018) (hbo original) november 12 banksy does new york (hbo original) november 14 tsukimichi – moonlit fantasy november 15 2022 rock & roll hall of fame induction ceremony (hbo original) november 25 the howard stern interview: bruce springsteen (hbo original) november 30 10,000 b.c. (2008) absolute power (1997) adam ruins everything, season 2-3 angels in the outfield (1951) the apparition (2012) the asphalt jungle (1950) badlands (1973) black sheep (1996) blade (1998) blade ii (2002) blade: trinity (2004) breach (2007) burn after reading (2008) the carbonaro effect, seasons 2-5 cats (2019) cleopatra (1963) comedy knockout critters (1986) critters 3 (1991) the curse of frankenstein (1957) dark shadows (2012) de blanco la patuda (aka white is for virgins) (2020) the descent (2006) the descent: part 2 (2009) diggers (2007) disclosure (1994) doc hollywood (1991) doctor sleep (2019) dracula a.d. 1972 (1972) dracula has risen from the grave (1969) draft day (2014) the drop (2014) first reformed (2018) the fly (1986) the forbidden kingdom (2008) freddy vs. jason (2003) freddy’s dead: the final nightmare (1991) friday the 13th (2009) gemini (2018) gone girl (2014) green room (2016) the haunting (1963) horror of dracula (1958) the hurt locker (2009) i origins (2014) if beale street could talk (2018) insidious (2010) into the forest (2016) journey 2: the mysterious island (2012) krisha (2016) lakeview terrace (2008) limitless (2011) los dias de la ballena (aka the days of the whale) (2019) lucas (1986) lucky you (2007) madagascar: escape 2 africa (2008) the mask (1994) the maze runner (2014) the middle moonfall (2022) the mummy (1959) narc (2003) the neverending story ii: the next chapter (1991) a nightmare on elm street (1984) a nightmare on elm street 2: freddy’s revenge (1985) a nightmare on elm street 3: dream warriors (1987) a nightmare on elm street 4: the dream master (1988) a nightmare on elm street 5: the dream child (1989) the outsiders (1983) paid off with michael torpey poltergeist (1982) pretty in pink (1986) private benjamin (1980) the purge: anarchy (2014) rachel dratch’s late night snack reindeer games (2000) the rookie (1990) safe haven (2013) the sea of trees (2016) see no evil, hear no evil (1989) the show (2017) signs (2002) some kind of wonderful (1987) soylent green (1973) stephen king’s cat’s eye (1985) talk show the game show team america: world police (2004) temptation: confessions of a marriage counselor (2013) urban cowboy (1980) v for vendetta (2005) wild rose (2019) young frankenstein (1974)
ValueCountFrequency (%)
the 200965
 
5.7%
of 100391
 
2.9%
and 96506
 
2.8%
to 94307
 
2.7%
in 76020
 
2.2%
a 63249
 
1.8%
for 36162
 
1.0%
is 33047
 
0.9%
on 30628
 
0.9%
that 30061
 
0.9%
Other values (110059) 2739299
78.3%
2024-02-15T15:58:01.098612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3517689
15.5%
e 2147616
 
9.5%
t 1595255
 
7.1%
a 1576392
 
7.0%
i 1394450
 
6.2%
o 1336444
 
5.9%
n 1335995
 
5.9%
s 1204029
 
5.3%
r 1197612
 
5.3%
h 747245
 
3.3%
Other values (884) 6572419
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17970759
79.4%
Space Separator 3529247
 
15.6%
Other Punctuation 508905
 
2.2%
Decimal Number 331093
 
1.5%
Control 90977
 
0.4%
Dash Punctuation 74552
 
0.3%
Final Punctuation 35263
 
0.2%
Open Punctuation 24050
 
0.1%
Close Punctuation 24029
 
0.1%
Initial Punctuation 16657
 
0.1%
Other values (14) 19614
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
131
 
4.3%
82
 
2.7%
81
 
2.7%
61
 
2.0%
47
 
1.6%
39
 
1.3%
39
 
1.3%
38
 
1.3%
37
 
1.2%
37
 
1.2%
Other values (515) 2420
80.3%
Lowercase Letter
ValueCountFrequency (%)
e 2147616
12.0%
t 1595255
 
8.9%
a 1576392
 
8.8%
i 1394450
 
7.8%
o 1336444
 
7.4%
n 1335995
 
7.4%
s 1204029
 
6.7%
r 1197612
 
6.7%
h 747245
 
4.2%
l 722331
 
4.0%
Other values (87) 4713390
26.2%
Other Symbol
ValueCountFrequency (%)
© 1066
59.8%
° 293
 
16.4%
® 129
 
7.2%
43
 
2.4%
¦ 23
 
1.3%
19
 
1.1%
12
 
0.7%
🇵 10
 
0.6%
👏 9
 
0.5%
8
 
0.4%
Other values (79) 172
 
9.6%
Other Punctuation
ValueCountFrequency (%)
, 215824
42.4%
. 191226
37.6%
" 28142
 
5.5%
' 24993
 
4.9%
: 15580
 
3.1%
/ 12757
 
2.5%
% 8334
 
1.6%
? 3561
 
0.7%
; 3297
 
0.6%
& 2078
 
0.4%
Other values (23) 3113
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
T 56
 
9.1%
S 53
 
8.6%
P 44
 
7.1%
N 41
 
6.6%
G 40
 
6.5%
D 37
 
6.0%
A 35
 
5.7%
O 30
 
4.9%
K 27
 
4.4%
M 26
 
4.2%
Other values (19) 229
37.1%
Decimal Number
ValueCountFrequency (%)
0 73598
22.2%
2 72189
21.8%
1 46305
14.0%
3 37305
11.3%
4 21003
 
6.3%
5 20379
 
6.2%
9 16819
 
5.1%
6 14968
 
4.5%
7 14488
 
4.4%
8 14023
 
4.2%
Other values (7) 16
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 1353
72.1%
| 273
 
14.6%
~ 105
 
5.6%
= 41
 
2.2%
38
 
2.0%
> 23
 
1.2%
11
 
0.6%
< 9
 
0.5%
6
 
0.3%
± 6
 
0.3%
Other values (6) 11
 
0.6%
Nonspacing Mark
ValueCountFrequency (%)
14
21.2%
11
16.7%
9
13.6%
́ 7
10.6%
6
9.1%
4
 
6.1%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (6) 7
10.6%
Open Punctuation
ValueCountFrequency (%)
( 23126
96.2%
[ 889
 
3.7%
14
 
0.1%
9
 
< 0.1%
4
 
< 0.1%
3
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 23110
96.2%
] 887
 
3.7%
14
 
0.1%
10
 
< 0.1%
3
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 10186
94.4%
411
 
3.8%
£ 116
 
1.1%
51
 
0.5%
¥ 18
 
0.2%
2
 
< 0.1%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
54
73.0%
ʻ 14
 
18.9%
ˇ 2
 
2.7%
ʺ 1
 
1.4%
1
 
1.4%
ˈ 1
 
1.4%
ː 1
 
1.4%
Modifier Symbol
ValueCountFrequency (%)
´ 8
28.6%
^ 6
21.4%
🏻 5
17.9%
🏽 4
14.3%
🏼 2
 
7.1%
` 2
 
7.1%
˘ 1
 
3.6%
Control
ValueCountFrequency (%)
82936
91.2%
8036
 
8.8%
 2
 
< 0.1%
€ 1
 
< 0.1%
Œ 1
 
< 0.1%
ƒ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3517689
99.7%
  11512
 
0.3%
24
 
< 0.1%
12
 
< 0.1%
10
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 58006
77.8%
10229
 
13.7%
6300
 
8.5%
16
 
< 0.1%
1
 
< 0.1%
Format
ValueCountFrequency (%)
 66
58.9%
24
 
21.4%
­ 8
 
7.1%
8
 
7.1%
6
 
5.4%
Other Number
ValueCountFrequency (%)
² 46
65.7%
10
 
14.3%
¹ 9
 
12.9%
½ 5
 
7.1%
Final Punctuation
ValueCountFrequency (%)
22107
62.7%
13041
37.0%
» 115
 
0.3%
Initial Punctuation
ValueCountFrequency (%)
15699
94.2%
956
 
5.7%
« 2
 
< 0.1%
Spacing Mark
ValueCountFrequency (%)
1
50.0%
1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1182
100.0%
Private Use
ValueCountFrequency (%)
4
100.0%
Enclosing Mark
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17971349
79.4%
Common 4650696
 
20.6%
Arabic 1110
 
< 0.1%
Han 595
 
< 0.1%
Hiragana 593
 
< 0.1%
Katakana 498
 
< 0.1%
Thai 246
 
< 0.1%
Inherited 21
 
< 0.1%
Greek 13
 
< 0.1%
Myanmar 12
 
< 0.1%
Other values (3) 13
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
11
 
1.8%
10
 
1.7%
10
 
1.7%
10
 
1.7%
10
 
1.7%
9
 
1.5%
9
 
1.5%
9
 
1.5%
9
 
1.5%
8
 
1.3%
Other values (263) 500
84.0%
Common
ValueCountFrequency (%)
3517689
75.6%
, 215824
 
4.6%
. 191226
 
4.1%
82936
 
1.8%
0 73598
 
1.6%
2 72189
 
1.6%
- 58006
 
1.2%
1 46305
 
1.0%
3 37305
 
0.8%
" 28142
 
0.6%
Other values (219) 327476
 
7.0%
Latin
ValueCountFrequency (%)
e 2147616
12.0%
t 1595255
 
8.9%
a 1576392
 
8.8%
i 1394450
 
7.8%
o 1336444
 
7.4%
n 1335995
 
7.4%
s 1204029
 
6.7%
r 1197612
 
6.7%
h 747245
 
4.2%
l 722331
 
4.0%
Other values (102) 4713980
26.2%
Arabic
ValueCountFrequency (%)
131
 
11.8%
82
 
7.4%
61
 
5.5%
39
 
3.5%
39
 
3.5%
38
 
3.4%
37
 
3.3%
32
 
2.9%
31
 
2.8%
28
 
2.5%
Other values (91) 592
53.3%
Katakana
ValueCountFrequency (%)
37
 
7.4%
34
 
6.8%
24
 
4.8%
24
 
4.8%
23
 
4.6%
21
 
4.2%
17
 
3.4%
17
 
3.4%
16
 
3.2%
16
 
3.2%
Other values (52) 269
54.0%
Hiragana
ValueCountFrequency (%)
81
 
13.7%
47
 
7.9%
35
 
5.9%
33
 
5.6%
30
 
5.1%
29
 
4.9%
23
 
3.9%
21
 
3.5%
21
 
3.5%
20
 
3.4%
Other values (36) 253
42.7%
Thai
ValueCountFrequency (%)
22
 
8.9%
20
 
8.1%
14
 
5.7%
12
 
4.9%
11
 
4.5%
10
 
4.1%
10
 
4.1%
9
 
3.7%
9
 
3.7%
8
 
3.3%
Other values (35) 121
49.2%
Myanmar
ValueCountFrequency (%)
2
16.7%
2
16.7%
က 2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Greek
ValueCountFrequency (%)
β 6
46.2%
τ 2
 
15.4%
ι 2
 
15.4%
ε 2
 
15.4%
α 1
 
7.7%
Inherited
ValueCountFrequency (%)
11
52.4%
́ 7
33.3%
̱ 2
 
9.5%
1
 
4.8%
Hangul
ValueCountFrequency (%)
2
33.3%
2
33.3%
1
16.7%
1
16.7%
Cyrillic
ValueCountFrequency (%)
ф 1
33.3%
ї 1
33.3%
а 1
33.3%
Unknown
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22535173
99.6%
Punctuation 68983
 
0.3%
None 18130
 
0.1%
Katakana 617
 
< 0.1%
CJK 594
 
< 0.1%
Hiragana 593
 
< 0.1%
Currency Symbols 464
 
< 0.1%
Thai 246
 
< 0.1%
Enclosed Alphanum Sup 68
 
< 0.1%
Letterlike Symbols 56
 
< 0.1%
Other values (20) 222
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3517689
15.6%
e 2147616
 
9.5%
t 1595255
 
7.1%
a 1576392
 
7.0%
i 1394450
 
6.2%
o 1336444
 
5.9%
n 1335995
 
5.9%
s 1204029
 
5.3%
r 1197612
 
5.3%
h 747245
 
3.3%
Other values (85) 6482446
28.8%
Punctuation
ValueCountFrequency (%)
22107
32.0%
15699
22.8%
13041
18.9%
10229
14.8%
6300
 
9.1%
956
 
1.4%
378
 
0.5%
150
 
0.2%
24
 
< 0.1%
24
 
< 0.1%
Other values (12) 75
 
0.1%
None
ValueCountFrequency (%)
  11512
63.5%
© 1066
 
5.9%
é 899
 
5.0%
ñ 466
 
2.6%
á 350
 
1.9%
° 293
 
1.6%
í 189
 
1.0%
ó 164
 
0.9%
ü 141
 
0.8%
131
 
0.7%
Other values (237) 2919
 
16.1%
Currency Symbols
ValueCountFrequency (%)
411
88.6%
51
 
11.0%
2
 
0.4%
Hiragana
ValueCountFrequency (%)
81
 
13.7%
47
 
7.9%
35
 
5.9%
33
 
5.6%
30
 
5.1%
29
 
4.9%
23
 
3.9%
21
 
3.5%
21
 
3.5%
20
 
3.4%
Other values (36) 253
42.7%
Katakana
ValueCountFrequency (%)
65
 
10.5%
54
 
8.8%
37
 
6.0%
34
 
5.5%
24
 
3.9%
24
 
3.9%
23
 
3.7%
21
 
3.4%
17
 
2.8%
17
 
2.8%
Other values (54) 301
48.8%
Letterlike Symbols
ValueCountFrequency (%)
43
76.8%
12
 
21.4%
1
 
1.8%
Thai
ValueCountFrequency (%)
22
 
8.9%
20
 
8.1%
14
 
5.7%
12
 
4.9%
11
 
4.5%
10
 
4.1%
10
 
4.1%
9
 
3.7%
9
 
3.7%
8
 
3.3%
Other values (35) 121
49.2%
Geometric Shapes
ValueCountFrequency (%)
19
42.2%
7
 
15.6%
7
 
15.6%
6
 
13.3%
2
 
4.4%
2
 
4.4%
2
 
4.4%
Alphabetic PF
ValueCountFrequency (%)
15
100.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 14
70.0%
ˇ 2
 
10.0%
˘ 1
 
5.0%
ʺ 1
 
5.0%
ˈ 1
 
5.0%
ː 1
 
5.0%
Math Operators
ValueCountFrequency (%)
11
73.3%
3
 
20.0%
1
 
6.7%
CJK
ValueCountFrequency (%)
11
 
1.9%
10
 
1.7%
10
 
1.7%
10
 
1.7%
10
 
1.7%
9
 
1.5%
9
 
1.5%
9
 
1.5%
9
 
1.5%
8
 
1.3%
Other values (262) 499
84.0%
VS
ValueCountFrequency (%)
11
100.0%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇵 10
14.7%
🇳 7
10.3%
🇦 6
 
8.8%
🇭 5
 
7.4%
🇿 5
 
7.4%
🇯 4
 
5.9%
🇧 4
 
5.9%
🇬 4
 
5.9%
🇨 4
 
5.9%
🇮 4
 
5.9%
Other values (9) 15
22.1%
Box Drawing
ValueCountFrequency (%)
8
53.3%
4
26.7%
2
 
13.3%
1
 
6.7%
Diacriticals
ValueCountFrequency (%)
́ 7
77.8%
̱ 2
 
22.2%
Emoticons
ValueCountFrequency (%)
🙌 6
26.1%
😡 4
17.4%
🙏 4
17.4%
😁 2
 
8.7%
😍 2
 
8.7%
😅 1
 
4.3%
😊 1
 
4.3%
🙂 1
 
4.3%
😘 1
 
4.3%
😃 1
 
4.3%
Math Alphanum
ValueCountFrequency (%)
𝐚 4
30.8%
𝐧 3
23.1%
𝐲 2
15.4%
𝐀 1
 
7.7%
𝐝 1
 
7.7%
𝐏 1
 
7.7%
𝕏 1
 
7.7%
PUA
ValueCountFrequency (%)
4
100.0%
Misc Symbols
ValueCountFrequency (%)
3
100.0%
Dingbats
ValueCountFrequency (%)
3
42.9%
2
28.6%
1
 
14.3%
1
 
14.3%
Myanmar
ValueCountFrequency (%)
2
16.7%
2
16.7%
က 2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Latin Ext Additional
ValueCountFrequency (%)
2
28.6%
2
28.6%
2
28.6%
1
14.3%
Arrows
ValueCountFrequency (%)
2
66.7%
1
33.3%
Arabic
ValueCountFrequency (%)
، 2
22.2%
م 2
22.2%
ا 1
11.1%
د 1
11.1%
ی 1
11.1%
ر 1
11.1%
ح 1
11.1%
Hangul
ValueCountFrequency (%)
2
33.3%
2
33.3%
1
16.7%
1
16.7%
Cyrillic
ValueCountFrequency (%)
ф 1
33.3%
ї 1
33.3%
а 1
33.3%
IPA Ext
ValueCountFrequency (%)
ɔ 1
100.0%
Alchemical
ValueCountFrequency (%)
🜃 1
100.0%

title_sentiment
Categorical

MISSING 

Distinct3
Distinct (%)0.1%
Missing42584
Missing (%)93.5%
Memory size711.8 KiB
Neutral
2050 
Negative
640 
Positive
282 

Length

Max length8
Median length7
Mean length7.3102288
Min length7

Characters and Unicode

Total characters21726
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowPositive
3rd rowNegative
4th rowNeutral
5th rowNeutral

Common Values

ValueCountFrequency (%)
Neutral 2050
 
4.5%
Negative 640
 
1.4%
Positive 282
 
0.6%
(Missing) 42584
93.5%

Length

2024-02-15T15:58:01.440405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T15:58:01.728628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral 2050
69.0%
negative 640
 
21.5%
positive 282
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 3612
16.6%
t 2972
13.7%
N 2690
12.4%
a 2690
12.4%
u 2050
9.4%
r 2050
9.4%
l 2050
9.4%
i 1204
 
5.5%
v 922
 
4.2%
g 640
 
2.9%
Other values (3) 846
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18754
86.3%
Uppercase Letter 2972
 
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3612
19.3%
t 2972
15.8%
a 2690
14.3%
u 2050
10.9%
r 2050
10.9%
l 2050
10.9%
i 1204
 
6.4%
v 922
 
4.9%
g 640
 
3.4%
o 282
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
N 2690
90.5%
P 282
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 21726
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3612
16.6%
t 2972
13.7%
N 2690
12.4%
a 2690
12.4%
u 2050
9.4%
r 2050
9.4%
l 2050
9.4%
i 1204
 
5.5%
v 922
 
4.2%
g 640
 
2.9%
Other values (3) 846
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3612
16.6%
t 2972
13.7%
N 2690
12.4%
a 2690
12.4%
u 2050
9.4%
r 2050
9.4%
l 2050
9.4%
i 1204
 
5.5%
v 922
 
4.2%
g 640
 
2.9%
Other values (3) 846
 
3.9%

Missing values

2024-02-15T15:57:38.074087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-15T15:57:39.254922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-15T15:57:40.169235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

article_idsource_idsource_nameauthortitledescriptionurlurl_to_imagepublished_atcontentcategoryfull_contentarticletitle_sentiment
089541NaNInternational Business TimesPaavan MATHEMAUN Chief Urges World To 'Stop The Madness' Of Climate ChangeUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon.https://www.ibtimes.com/un-chief-urges-world-stop-madness-climate-change-3716939https://d.ibtimes.com/en/full/4496078/nepals-glaciers-melted-65-percent-faster-last-decade-previous-one-said-guterres-who.jpg2023-10-30 10:12:35.000000UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastatin… [+2444 chars]NepalUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon. "The rooftops of the world are caving in," Guterres said on a visit to the Everest region in mountainous Nepal, adding that the country had lost nearly a third of its ice in just over three decades. "Glaciers are icy reservoirs -- the ones here in the Himalayas supply fresh water to well over a billion people," he said. "When they shrink, so do river flows." Nepal's glaciers melted 65 percent faster in the last decade than in the previous one, said Guterres, who is on a four-day visit to Nepal. Glaciers in the wider Himalayan and Hindu Kush ranges are a crucial water source for around 240 million people in the mountainous regions, as well as for another 1.65 billion people in the South Asian and Southeast Asian river valleys below. The glaciers feed 10 of the world's most important river systems, including the Ganges, Indus, Yellow, Mekong and Irrawaddy, and directly or indirectly supply billions of people with food, energy, clean air and income. Scientists say they are melting faster than ever before due to climate change, exposing communities to unpredictable and costly disasters. "I am here today to cry out from the rooftop of the world: stop the madness", Guterres said, speaking from Syangboche village, with the icy peak of the world's highest mountain Everest towering behind him. "The glaciers are retreating, but we cannot. We must end the fossil fuel age," he said. The world has warmed an average of nearly 1.2 degrees Celsius since the mid-1800s, unleashing a cascade of extreme weather, including more intense heatwaves, more severe droughts and storms made more ferocious by rising seas. Hardest hit are the most vulnerable people and the world's poorest countries, which have done little to contribute to the fossil fuel emissions that drive up temperatures. "We must act now to protect people on the frontline, and to limit global temperature rise to 1.5 degrees, to avert the worst of climate chaos," Guterres said. "The world can't wait." In the first phase of climate change's effects, melting glaciers can trigger destructive floods. "Melting glaciers mean swollen lakes and rivers flooding, sweeping away entire communities", he added. But all too soon, glaciers will dry up if change is not made, he warned. "In the future, major Himalayan rivers like the Indus, the Ganges and Brahmaputra could have massively reduced flows, he said. "That spells catastrophe".NaNNaN
189542NaNPrtimes.jpNaNRANDEBOOよりワンランク上の大人っぽさが漂うニットとベストが新登場。[株式会社Ainer]\nRANDEBOO(ランデブー)では2023年7月18日(火)より公式WEBストアにて2023 Autumn Winter コレクションの発売を開始。\nシーズンコレクションとして、"Nepal Handmade ram vest"と”Nepal Handmade ram kn...https://prtimes.jp/main/html/rd/p/000000147.000032220.htmlhttps://prtimes.jp/i/32220/147/ogp/d32220-147-21916971e381da87f7a9-0.jpg2023-10-06 04:40:02.000000RANDEBOO2023718()WEB2023 Autumn Winter \n"Nepal Handmade ram vest"Nepal Handmade ram knit"2023106()12:00WEB\n106() 12:00\nhttps://x.gd/226uY\nNepal Handmade ram vest\n¥17,600()\nGray/Ivory\n[]\n1\n[D… [+583 chars]NepalNaNNaNNaN
289543NaNVOA Newswebdesk@voanews.com (Agence France-Presse)UN Chief Urges World to 'Stop the Madness' of Climate ChangeUN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon.\n\n\n"The rooftops of the world are…https://www.voanews.com/a/un-chief-urges-world-to-stop-the-madness-of-climate-change-/7332605.htmlhttps://gdb.voanews.com/01000000-0a00-0242-60fd-08dbd93618b1_w1200_r1.jpg2023-10-30 10:53:30.000000Kathmandu, Nepal  UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witn… [+2468 chars]NepalNaNNaNNaN
389545NaNThe Indian ExpressEditorialSikkim warning: Hydroelectricity push must be accompanied by safety measuresEcologists caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday's disaster is a warning to take such caveats seriouslyhttps://indianexpress.com/article/opinion/editorials/sikkim-warning-hydroelectricity-push-must-be-accompanied-by-safety-measures-8970365/https://images.indianexpress.com/2023/10/edit-4.jpg2023-10-06 01:20:24.000000At least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem… [+1826 chars]NepalAt least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem to have been triggered by a combination of factors. A cloud burst ripped apart the South Shonak Lake — a glacial body in the state’s northwest. According to Sikkim’s Disaster Management Authority (SDMA), the Teesta has inundated at least four districts in the state. The calamity was aggravated by the release of water from the Chungthang dam — initial reports suggest that the breach was caused by water rushing from the mountains. Disaster management authorities are also investigating the possibility of the event being triggered by an earthquake in Nepal on Tuesday. The probe is likely to throw more light on the immediate causes of the flash flood but one thing has long been clear — states in the Himalayan region must respect the fragile ecology of the mountains and put in adequate safeguards to mitigate the damage caused by increasingly frequent extreme rainfall events. For years, studies have red-flagged the South Shonak Lake’s expansion due to glacial melting and warned that the water body is susceptible to breaches. In 2021, for instance, a study by scientists from IIT Roorkee, Indian Institute of ScienceBangalore, University of Dayton, USA, University of Graz, Austria, and the Universities of Zurich and Geneva in Switzerland recommended regular monitoring of the lake’s growth and continuous assessment of the region’s slope stability. The National Disaster Management Authority guidelines also say that risk reduction has to begin with mapping such water bodies, taking structural measures to prevent their breach and establishing mechanisms that can alert people about glacial lake outbursts. The IMD has collaborated with the US National Weather Service to warn people about six to 24 hours before a flash flood. But the system doesn’t seem to have come to terms with the Himalayan region’s idiosyncrasies. The Northeast has a key place in the hydel power push of successive governments at the Centre. The Chungthang Dam is a part of the 1,200 MW Teesta Stage 3 Hydroelectric Project. The government claims that such projects are climate-friendly because of their low emissions intensity. Hydroelectric power is also a major source of revenue for Sikkim. Ecologists, however, caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday’s disaster is a warning to take such caveats seriously and install robust safety mechanisms.NaNNaN
489547NaNThe Times of IsraelJacob Magid200 foreigners, dual nationals cut down in Hamas assault. Here’s where they were fromFrance lost 35 citizens, Thailand 33, US 31, Ukraine 21, Russia 19, UK 12, Nepal 10, Germany 10, Argentina 9, Canada 6, Romania 5, Portuguese 4, China 4, Philippines 4, Austria 4\nThe post 200 foreigners, dual nationals cut down in Hamas assault. Here’s where …https://www.timesofisrael.com/200-foreigners-dual-nationals-cut-down-in-hamas-assault-heres-where-they-were-from/https://static.timesofisrael.com/www/uploads/2023/10/231011160737-emily-hand-beeri-victim.jpg2023-10-27 01:08:34.000000Scores of foreign citizens were killed, taken hostage or listed as missing after the Hamas terror group launched a major assault on Israel on October 7.\nMore than 1,400, mainly civilians, have been … [+4658 chars]NepalNaNNaNNaN
589550NaNBBC Newshttps://www.facebook.com/bbcworldservice/中印交惡令尼泊爾機場難以發展國際航線尼泊爾花費數百萬美元建造一座新機場,希望藉此推動旅遊業——但是機場建好了,卻沒有人來。https://www.bbc.com/zhongwen/trad/world-67015581https://ichef.bbci.co.uk/news/1024/branded_zhongwen/1377C/production/_131304797_gettyimages-1240701029.jpg2023-10-05 07:30:45.000000Getty Images\n20225\n·Bishnu Sharma\nLumbini\nLumbini Development Trust2022100760061005Gautam Buddha International Airport\nBhairahawa Airport250155\nBBC\nBBC/Anbarasan Ethirajan\n12\nGetty Images\n2… [+196 chars]NepalNaNNaNNaN
689551al-jazeera-englishAl Jazeera EnglishKaushik RajPro-Israel rallies allowed in India but Palestine solidarity sees crackdownIndia, the first non-Arab country to recognise the PLO, is now seen closer to Israel and its biggest benefactor, the US.https://www.aljazeera.com/news/2023/10/25/pro-israel-rallies-allowed-in-india-but-palestine-solidarity-sees-crackdownhttps://www.aljazeera.com/wp-content/uploads/2023/10/2023-10-20T131413Z_1502521591_RC2AW3AED0YK_RTRMADP_3_ISRAEL-PALESTINIANS-INDIA-PROTEST-1698223702.jpg?resize=1920%2C14402023-10-25 09:58:17.000000New Delhi, India Israels relentless bombing of the besieged Gaza Strip and killing of nearly 6,000 people a third of them children in two weeks has outraged people across the world, triggering mass p… [+9013 chars]NepalIndia, the first non-Arab country to recognise the PLO in the 1970s, is now seen closer to Israel and its biggest benefactor, the United States. New Delhi, India– Israel’srelentless bombingof the besieged Gaza Strip and killing of nearly 6,000 people – a third of them children – in two weeks has outraged people across the world, triggering mass protests and a call for an immediate ceasefire. However, in India – the first non-Arab country to recognise the Palestine Liberation Organization (PLO), but now seen closer to Israel and its biggest benefactor, the United States – some pro-Palestine protesters reported being targeted by the government. Less than a week after the Gaza assault began, police in Hamirpur district of India’s most populous Uttar Pradesh state were looking for Muslim scholars Atif Chaudhary and Suhail Ansari. Their alleged crime: putting a WhatsApp display photo that said: “I stand with Palestine.” The two men were charged with promoting enmity between social groups. Ansari is under arrest, while Chaudhary is on the run, according to the police. In the same state, governed by the Hindu nationalist Bharatiya Janata Party (BJP), four students of the Aligarh Muslim University were booked by the police after they took out a pro-Palestine march on the campus a day after the Gaza assault began on October 7. However, when the Hindu far-right group Bajrang Dal took out a pro-Israel march in the same Aligarh city, raising slogans such as “Down with Palestine, Down with Hamas”, no action was taken against them by the authorities. In the national capital, New Delhi, there have been several examples of people being detained during rallies organised by student groups, activists and citizens for solidarity with the Palestinians since October 7. In the western state of Maharashtra, also governed by the BJP in alliance with a regional party, two protesters, Ruchir Lad and Supreeth Ravish, were arrested on October 13 for holding a march against the war on Gaza and charged with unlawful assembly. Pooja Chinchole, member of the Revolutionary Workers Party of India and one of the organisers of the protest held in state capital Mumbai, told Al Jazeera the police “created many hurdles before us when they got to know that we are organising a pro-Palestine protest”. “They detained one of the organisers a day before the protest and three organisers on the morning of the protest. When we still gathered to protest, they snatched our microphone, placards, and after a while, started using force on some of us,” she said. The crackdown, however, was not limited to the BJP-ruled states only. In the southern Karnataka state, governed by the main opposition Congress party, police charged 10 activists with creating a public nuisance after they organised a silent march in support of the Palestinians on October 16 in Bengaluru, the capital of the state. The Karnataka police also arrested a 58-year-old Muslim man for allegedly posting a video in support of Hamas on WhatsApp. Police also briefly detained Alam Nawaz, a Muslim government employee, for updating his WhatsApp status with a Palestinian flag and “Long Live Palestine” message. “People started seeing me with suspicion as if I have committed some crime by expressing my solidarity with Palestinian people,” Nawaz, 20, told Al Jazeera. All this despite the Congress expressing its support for the “rights of the Palestinian people to land, self-government and to live with dignity” as the party called for an immediate ceasefire in a resolution passed by its working committee on October 9. Meanwhile, pro-Israel rallies, organised mainly byHindu right-wing groups, were seen across India, while many on social media offered their services to the Israeli forces. On Saturday, dozens of supporters of a retired Indian army soldier travelled 182km (113 miles) to reach the Israeli embassy in New Delhi where they offered to go to Israel to fight against the Palestinians in Gaza. Last week, one of India’s most influential Hindu nationalists, Yati Narsinghanand, released a video in which he said Hindus and Jews “have the same enemy: Muhammad and his satanic book” as he urged the Israeli government to allow 1,000 Hindus to settle in Israel in order to “take on those Muslims”. Israel’s ambassador to India, Naor Gilon, on October 8 said he had received several requests from Indians wanting to voluntarily fight for Israel. Apoorvanand, professor of Hindi language at Delhi University, told Al Jazeera he was not surprised that the Hindu far right, which openly admires Adolf Hitler for his action against the Jews, is now supporting the Zionists in Israel. “Hindu far-right organisations in India have always supported those who dominate by violence. Hitler did once, so they supported him. Now Israel is doing this, so they are supporting it,” he said. Apoorvanand said the Hindu right in India thinks there are ideological linkages between them and the Zionists in Israel. “It looks like Israel is fighting a proxy war on behalf of the Hindu far right. They think Israel is fighting and decimating Muslims on their behalf. The way they want to establish Akhand Bharat [Unified India] by joining Pakistan, Afghanistan, Nepal together with India, they think Israel is following the same expansionist ideology,” he said. This was not always the case. India’s foreign policy has historically supported the Palestinian cause, which began with India voting against the United Nations resolution to create the state of Israel in 1947 and then recognising the PLO as a representative of the Palestinian people in 1974. India’s pro-Palestine stand was guided by the shared history of colonisation by the British, Zikrur Rahman, former Indian ambassador to Palestine, told Al Jazeera. “In the postcolonial era, we identified that this is a colonial attempt to divide the country and to create another country. We were not in favour of the creation of a country on the basis of religion,” he said. Rahman, however, added that while India’s position on Palestine has not changed, it is not as strong as it used to be. India recognised the creation of Israel in 1950, but did not establish diplomatic relations until 1992, when the details of the firstOslo Accordwere being finalised. Since then, India has tried to strike a balance between its strategic relations with Israel and sympathising with the Palestinian struggle. Today, India is the largest buyer of Israeli-made weapons, while strategic and security cooperation between them has grown manifold. Comparisons have also been made between Israel demolishing homes of Palestinians in the occupied territories and a similar policy adopted by some BJP state governmentsmainly against Muslimsas forms of “collective punishment” of the community. Since Prime Minister Narendra Modi came to power in 2014, he has made public statements, calling his Israeli counterpart Benjamin Netanyahu a “good friend” on several occasions. Modi was one of the first global leaders to post his solidarity with Israel after Hamas’s unprecedented incursion on October 7. “Deeply shocked by the news of terrorist attacks in Israel,” said his post on X, which came four hours before US President Joe Biden reacted to the event. Modi also condemned the Israeli attack onal-Ahli Arab Hospitalin Gaza on October 18, in which nearly 500 Palestinians were killed, though his message on X appeared nearly eight hours after Biden’s post. Meanwhile, India’s Ministry of External Affairs issued a statement on October 12, reiterating New Delhi’s position of establishing a “sovereign, independent, and viable state of Palestine, living within secure and recognised borders, side by side at peace with Israel”. Last week, Modi posted on X about his phone call with the Palestinian Authority President Mahmoud Abbas, in which he repeated India’s “longstanding principled position on the Israel-Palestine issue”. He said his government is sending humanitarian assistance for the besieged residents of Gaza. Journalist Anand K Sahay, however, thinks India’s response to the unfolding humanitarian disaster in Gaza has not been adequate. “What India didn’t say is important. India didn’t demand a ceasefire. Historically, India has always demanded a ceasefire in case of a [foreign] war. In this case also we should have strongly said: stop the war,” he told Al Jazeera. Sahay said Modi’s flaunting of closeness with Israel is also aimed at appeasing his core vote bank: the Hindus. “Suppose there was another religion in majority in Palestine. Then our stand may have been different. During the Russia-Ukraine war, we said ‘this is not an age of war’. Why couldn’t we say this in case of Israel-Palestine war?” asked Sahay. “By not asking for a ceasefire, India was also indirectly signalling the US that the Indian position was very close to the US line.” Follow Al Jazeera English:NaNNaN
789555NaNThe Indian ExpressNew York TimesNo nation in the world is buying more planes than India. Here’s why.India's largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparalleled in aviation.https://indianexpress.com/article/business/aviation/no-nation-world-buying-more-planes-than-india-here-why-9010202/https://images.indianexpress.com/2023/11/igiairport.jpg2023-11-02 05:48:58.000000No nation in the world is buying as many airplanes as India. Its largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparall… [+7083 chars]NepalWritten by Alex Travelli and Hari Kumar No nation in the world is buying as many airplanes as India. Its largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparalleled in aviation. In NewDelhi, Indira Gandhi International Airport will be ready for 109 million passengers next year, as it prepares to become the world’s second busiest, behind Hartsfield-Jackson Atlanta International Airport in the United States. And this is happening in a vast country still heavily reliant on trains — with 20 journeys by rail for every one by air. The enormous aviation build-out, with a surge of investment behind it, has pride of place in India’s case for a greater standing on the world stage. As it moves up the ranks of the world’s biggest economies, India is scrambling to meet the expanding ambitions of its ascendant middle class. Its airports present highly visible achievements. Air travel remains out of the financial reach of most Indians. An estimated 3% of the country’s population flies on a regular basis. But in a nation of 1.4 billion people, that percentage represents 42 million — executives, students and engineers who yearn to get quickly from here to there inside India’s borders, and to gain easier access to destinations beyond, for both business and vacation. Kapil Kaul, CEO of CAPA India, an advisory firm focused on aviation, calls “the next two to three years critical for achieving the quality of growth that India desires and deserves.” Growth has so far been profitless. Now Indian aviation must prove it can make money. The effects of the spending spree should redound across India’s economy. Cargo comes with passenger traffic, and foreign investment tends to follow closely behind, Kaul said. Arrivals to the international terminal at Indira Gandhi Airport are greeted by a wall of giant sculptural hands, their fingers and palms folded into the signifying shapes of the Buddha’s gestures, looking both ancient and futuristic. In 2012, when they were installed, 30 million passengers passed through the airport. By the time the airport has expanded to its new capacity, another one will have been built from scratch on the other side of the city. Indira Gandhi Airport is racing to get bigger. In July it added a fourth runway and opened an elevated taxiway. The company that operates it, GMR Airports, took over in 2006, a time when all arrivals walked past cows lazing in the dust to reach a taxi stand. By 2018 the facility was rated as India’s most valuable infrastructural asset. To spare the use of jet fuel, a battery-powered TaxiBot lugs idling planes around the tarmac. An automated luggage-handling system can sort 6,000 bags an hour. Two beneficiaries of India’s expanding aviation market are the world’s largest airplane makers: Boeing in America and Airbus in Europe. In February, Air India, which Tata Group took private last year, agreed to buy 250 planes from Airbus and 220 from Boeing, worth a combined $70 billion. In June, IndiGo, the country’s biggest carrier by passengers and flights, ordered 500 new Airbus A230s. The bulk of the growth of Indian aviation has been among homegrown airlines, which have clocked a 36% increase in passengers since 2022. Foreign tourist arrivals are rebounding since the pandemic, but are still relatively scarce, barely topping 10 million in a good year (about the same as Romania). So low-cost carriers are adding new countries to their destinations in order to accommodate India’s demand for foreign tourism. Azerbaijan, Kenya and Vietnam are all a direct flight from Delhi orMumbai, India’s financial capital, for less than 21,000 rupees ($250) one way. The air corridor between Delhi and Mumbai was already one of the world’s 10 busiest. Like Delhi, Mumbai has new airport terminals that would be the envy of any city in America, not to mention the glorious new all-bamboo Terminal 2 at Kempegowda International Airport in Bengaluru, a city in southern India. But the expansion in infrastructure is not limited to the country’s premier metropolitan areas. Prime MinisterNarendra Modi’s government likes to point out that the number of airports has doubled in the nine years since he took office, to 148 from 74. Jyotiraditya Scindia, Modi’s aviation minister, said there would be at least 230 by 2030. The government has invested more than $11 billion in airports over the past decade, and Scindia has promised another $15 billion. That means that sleepy towns such asDarbhanga, a former principality in the impoverished state of Bihar in eastern India, now have nonstop access to Delhi, Bengaluru and beyond. For many of the 900 travelers a day who fill its flights, including plenty from nearby Nepal, the new airport has transformed the journey. Prasanna Kumar Jha, 52, was born in Darbhanga but works in Delhi as a tax consultant. “Who ever expected that Darbhanga would be on the air map?” he asked. Flying to his hometown on short notice to see his ailing mother cost him 10,500 rupees ($126), which pinched. “But if you calculate the alternative — by train from Delhi and then taxi to Darbhanga — it will take at least 30 hours,” he said. “The plane journey is no longer a luxury but a necessity.” Darbhanga’s airport is a far cry from New Delhi’s. There is no parking lot. Passengers walk from the edge of a highway past a checkpoint to wait on benches outside the terminal. Then they wait on another set of outdoor benches after clearing the security check. But it works. Another passenger on the same flight at Darbhanga, Ajay Jha, was cradling his 1-year-old daughter, Saranya, as he stood near the rudimentary baggage claim. His family was on the last leg of a trip that started in Bellevue, Washington, where he works as an engineer for Amazon, to a family reunion in the Bihari countryside. Traveling halfway around the world took less time than Jha used to spend getting home from his school in Bengaluru. Yet a vast majority of Indians cannot afford such conveniences. The annual mean income is still less than a single economy-class fare from the United States, and, in this top-heavy economy, most Indians earn much less than that. Middle class, in Indian parlance, indicates somewhere close to the top of the pyramid. A report by CAPA India counted just 0.13 passenger seats per capita in 2019 for Indians, compared with 0.52 for Chinese and 3.03 for Americans. But aviation companies and India’s elected officials look at the low penetration and see opportunity. A scarcity of competition, in the face of an emerging duopoly between IndiGo and the Tata-led airlines, is one of the new landscape’s most striking features. Smaller competitors keep going bust, most recently Go First, which declared bankruptcy in May. A shortage of pilots, after dozens were poached by bigger companies, forced Akasa Air, a promising upstart, to cancel flights in August. But supply shortages are not the worst kind of problem to have in today’s global economy. With aviation’s growth in the decade before the pandemic steady at about 15% a year, the Indian boom seems all but guaranteed to change the future of aviation worldwide. If the benefits accruing to the winners in India’s economy can be coaxed into trickling outward and downward, the same could go for many other sectors.NaNNaN
889556abc-news-auABC News (AU)ABC NewsMore than 130 people have reportedly been taken to Gaza as hostages. Here's what we knowHamas fighters have escaped back to Gaza with dozens of captives, including women, children and the elderly, after an unprecedented attack that has seen at least 700 people killed in Israel, followed by more than 400 in the Palestinian territories after retal…https://www.abc.net.au/news/2023-10-09/hamas-takes-hostages-to-gaza/102950956https://live-production.wcms.abc-cdn.net.au/ec96e8c1e1aca987e5156a05e3429b91?impolicy=wcms_crop_resize&cropH=450&cropW=800&xPos=0&yPos=75&width=862&height=4852023-10-09 01:09:03.000000Hamas fighters have escaped back to Gaza with dozens of captives following the group's unprecedented attack on Israel which saw at least 700 Israelis killed, followed by 400 Palestinians after retali… [+1553 chars]NepalNaNNaNNaN
989837bbc-newsBBC NewsNaNWorld Cup: New Zealand v Bangladesh - clips, radio & textFollow live text, in-play video clips and radio commentary as New Zealand play Bangladesh in the Men's Cricket World Cup 2023.https://www.bbc.co.uk/sport/live/cricket/66854573https:////m.files.bbci.co.uk/modules/bbc-morph-sport-seo-meta/1.23.3/images/bbc-sport-logo.png2023-10-12 14:27:52.000000Here we are then. Match 11 of the Cricket World Cup. A week and a day into the tournament and we've seen records broken, plenty of passion and a decent helping of DRS controversy.\nWhat we haven't ha… [+252 chars]New ZealandNaNNaNNaN
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32623626996NaNXDA DevelopersKarthik IyerFive ways Microsoft can improve the Xbox Game Bar on PCHere are a few ways Microsoft can improve the Xbox Game Bar on PC to improve the overall gaming experience.https://www.xda-developers.com/five-ways-microsoft-improve-xbox-game-bar/https://static1.xdaimages.com/wordpress/wp-content/uploads/wm/2023/11/xbox-game-bar-on-a-windows-laptop.jpg2023-11-22 11:00:21.000000Key Takeaways\r\n<ul><li> The Xbox Game Bar needs to add advanced recording options, including support for 4K recording and higher frame rates, to keep up with modern gaming technology. </li><li> The l… [+7974 chars]GamesNaNNaNNaN
32624626997espnESPNJenna LaineBucs hoping 'small things' don't hold them back from winning, playoffsDespite being 4-6, the Bucs are still only one game behind the Saints in the NFC South with key divisional games looming down the stretch.https://www.espn.com/nfl/story/_/id/38952788/bucs-hope-small-things-hold-back-playoffshttps://a2.espncdn.com/combiner/i?img=%2Fphoto%2F2023%2F1121%2Fr1256196_1296x729_16%2D9.jpg2023-11-22 11:00:10.000000TAMPA, Fla. -- Despite a 27-14 loss at the San Francisco 49ers on Sunday, the Tampa Bay Buccaneers, at 4-6, are still hopeful.Coach Todd Bowles emphasized after the Week 11 loss at Levi's Stadium tha… [+4858 chars]GamesNaNNaNNaN
32625626998NaNLa VanguardiaSilvia FernándezGoogle estuvo cerca de comprar Epic Games antes de lanzar StadiaHace unos meses, concretamente el 18 de enero de este año, Google decidió cerrar definitivamente su servicio para videojuegos en la nube: Stadia. Antes de desactivar sus servidores, el gigante tecnológico quiso dar una última alegría a sus usuarios, así que a…https://www.lavanguardia.com/andro4all/google/google-cerca-comprar-epic-games-antes-lanzar-stadiahttps://www.lavanguardia.com/andro4all/hero/2023/11/google-epic-games-ok.png?width=12002023-11-22 11:19:34.000000La idea de Google era que Fortnite se convirtiera en una pieza clave en Android\r\nHace unos meses, concretamente el 18 de enero de este año, Google decidió cerrar definitivamente su servicio para vide… [+3070 chars]GamesNaNNaNNaN
32626626999techradarTechRadarRhys WoodGameMaker engine transforms its business model and introduces a one-time feeYoYo Games has changed its business model for the GameMaker engine, making it much more accessible.https://www.techradar.com/gaming/consoles-pc/gamemaker-engine-transforms-its-business-model-and-introduces-a-one-time-feehttps://cdn.mos.cms.futurecdn.net/AC4kemFeGFmGqXhKyeGEFP-1200-80.png2023-11-22 10:13:44.000000YoYo Games, creator of the popular GameMaker engine, has switched up its pricing model and appears to be much more accessible as a result.\r\nAn official blog post written by GameMaker head Russell Kay… [+1643 chars]GamesNaNNaNNaN
32627627000ignIGNLuke ReillyBluey: The Videogame ReviewBluey: The Videogame may look the part but, with shonky controls and barely two hours of gameplay, everywhere else it’s a dog’s breakfast.https://www.ign.com/articles/bluey-the-videogame-reviewhttps://assets-prd.ignimgs.com/2023/11/22/bluey-thumb-1700628294058.png?width=12802023-11-22 08:14:17.000000The very same weekend we received Bluey: The Videogame, Australias ABC broadcasted a countdown of the top 100 Bluey episodes, as voted by the public. Tuning in for the final 20-or-so, it was the perf… [+5943 chars]GamesNaNNaNNaN
32628627001NaNAscii.jpASCIIなんと1年ぶり!『フォートナイト』リアルタイムイベント「BIG BANG」を12月3日4時に開催決定2023年11月22日、Epic Gamesはバトルロイヤルゲーム『Fortnite』(フォートナイト)にて、12月3日4時(日本時間)より、リアルタイムイベント「BIG BANG(ビッグバン)」を開催すると発表した。https://weekly.ascii.jp/elem/000/004/170/4170864/https://ascii.jp/img/2023/11/22/3643518/l/56d5fc71b95cf6e8.jpg2023-11-22 05:00:00.000000Epic Games1122Fortnite20231234\r\nhttps://www.fortnite.com/news/the-big-bang-event-a-new-beginning-for-fortnite-on-december-2\r\n1\r\n3 1\r\n4 1https://ascii.jp/elem/000/004/116/4116037/\r\n20231121\r\n1\r\n48……\r\n… [+316 chars]GamesNaNNaNNaN
32629627002NaNRock Paper ShotgunEdwin Evans-ThirlwellThe grandest factory sim ever made is getting an interplanetary combat system this DecemberDyson Sphere Program is already rather overwhelming - this is, after all, a strategy sim about wrapping whole planets in conveyor belts so as to build cages around stars and harvest their juices - and now the absolute madfolk of Youthcat Studio are adding com…https://www.rockpapershotgun.com/the-grandest-factory-sim-ever-made-is-getting-an-interplanetary-combat-system-this-decemberhttps://assetsio.reedpopcdn.com/1_79VFemo.jpg?width=1200&height=630&fit=crop&enable=upscale&auto=webp2023-11-22 10:38:48.000000Dyson Sphere Program is already rather overwhelming - this is, after all, a strategy sim about wrapping whole planets in conveyor belts so as to build cages around stars and harvest their juices - an… [+2648 chars]GamesNaNNaNNaN
32630627003NaNSamMobileSagar NareshGoogle tried to buy a controlling stake in Epic Games before Stadia launchedSome interesting titbits have popped up in the ongoing Epic Games vs Google trial. According to the latest emails that have popped up, Google tried to buy out Epic Games even before Stadia, the dead cloud-gaming platform, was launched. Google thought of this …https://www.sammobile.com/news/google-tried-to-buy-a-controlling-stake-in-epic-games-before-stadia-launched/https://www.sammobile.com/wp-content/uploads/2022/09/Google-Stadia-720x480.jpg2023-11-22 05:06:35.000000Some interesting titbits have popped up in the ongoing Epic Games vs Google trial. According to the latest emails that have popped up, Google tried to buy out Epic Games even before Stadia, the dead … [+1836 chars]GamesNaNNaNNaN
32631627004NaNMIT Technology ReviewZeyi YangThis Chinese map app wants to be a super app for everything outdoorsThis story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday. Thanksgiving is almost here. This year, when you get together with your family, may I suggest a fun lit…https://www.technologyreview.com/2023/11/22/1083805/chinese-map-super-app-game/https://wp.technologyreview.com/wp-content/uploads/2023/11/super-map.jpg?resize=1200,6002023-11-22 11:00:00.000000Amap, for example, is one of the most widely used map and navigation apps in China today. But when I open it on my phone, I can see over 30 functions that you wouldnt find on Western-equivalent apps.… [+3234 chars]GamesNaNNaNNaN
32632627005NaNPC GamerHarvey RandallAn Armored Core 6 co-op mod has a fully-functional alpha, though its creator wants to add a few key features like difficulty scaling firstLukeYui's been adding multiplayer functionality to FromSoftware games for a while—and Armored Core 6 is next in line.https://www.pcgamer.com/an-armored-core-6-co-op-mod-has-a-fully-functional-alpha-though-its-creator-wants-to-add-a-few-key-features-like-difficulty-scaling-first/https://cdn.mos.cms.futurecdn.net/u6BEypjgPabDwNaNNaNNaNNaNNaNNaN

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